Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to ...Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures.展开更多
With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods ...With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.展开更多
To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network mo...To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network model is proposed by fusing multi-scale feature information.Firstly,a multi-scale feature extraction module is designed to obtain multi-scale information on feature images by using different scales of convolution kernels.Meanwhile,the channel attention mechanism is used to increase the global information acquisition of the network.Secondly,the feature images processed by the multi-scale feature extraction module are fused with the deep feature images through short links to guide the full learning of the network,thus reducing the loss of texture details of the deep network feature images,and improving network generalization ability and recognition accuracy.Finally,the validity of the MSFResNet model is verified using public datasets and applied to wild mushroom identification.Experimental results show that compared with ResNeXt50 network model,the accuracy of the MSFResNet model is improved by 6.01%on the FGVC-Aircraft common dataset.It achieves 99.13%classification accuracy on the wild mushroom dataset,which is 0.47%higher than ResNeXt50.Furthermore,the experimental results of the thermal map show that the MSFResNet model significantly reduces the interference of background information,making the network focus on the location of the main body of wild mushroom,which can effectively improve the accuracy of wild mushroom identification.展开更多
Globally,diabetic retinopathy(DR)is the primary cause of blindness,affecting millions of people worldwide.This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure p...Globally,diabetic retinopathy(DR)is the primary cause of blindness,affecting millions of people worldwide.This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective treatment.Deep learning-based automated diagnosis for diabetic retinopathy can facilitate early detection and treatment.However,traditional deep learning models that focus on local views often learn feature representations that are less discriminative at the semantic level.On the other hand,models that focus on global semantic-level information might overlook critical,subtle local pathological features.To address this issue,we propose an adaptive multi-scale feature fusion network called(AMSFuse),which can adaptively combine multi-scale global and local features without compromising their individual representation.Specifically,our model incorporates global features for extracting high-level contextual information from retinal images.Concurrently,local features capture fine-grained details,such as microaneurysms,hemorrhages,and exudates,which are critical for DR diagnosis.These global and local features are adaptively fused using a fusion block,followed by an Integrated Attention Mechanism(IAM)that refines the fused features by emphasizing relevant regions,thereby enhancing classification accuracy for DR classification.Our model achieves 86.3%accuracy on the APTOS dataset and 96.6%RFMiD,both of which are comparable to state-of-the-art methods.展开更多
Human pose estimation has received much attention from the research community because of its wide range of applications.However,current research for pose estimation is usually complex and computationally intensive,esp...Human pose estimation has received much attention from the research community because of its wide range of applications.However,current research for pose estimation is usually complex and computationally intensive,especially the feature loss problems in the feature fusion process.To address the above problems,we propose a lightweight human pose estimation network based on multi-attention mechanism(LMANet).In our method,network parameters can be significantly reduced by lightweighting the bottleneck blocks with depth-wise separable convolution on the high-resolution networks.After that,we also introduce a multi-attention mechanism to improve the model prediction accuracy,and the channel attention module is added in the initial stage of the network to enhance the local cross-channel information interaction.More importantly,we inject spatial crossawareness module in the multi-scale feature fusion stage to reduce the spatial information loss during feature extraction.Extensive experiments on COCO2017 dataset and MPII dataset show that LMANet can guarantee a higher prediction accuracy with fewer network parameters and computational effort.Compared with the highresolution network HRNet,the number of parameters and the computational complexity of the network are reduced by 67%and 73%,respectively.展开更多
The precise and automatic segmentation of prostate magnetic resonance imaging(MRI)images is vital for assisting doctors in diagnosing prostate diseases.In recent years,many advanced methods have been applied to prosta...The precise and automatic segmentation of prostate magnetic resonance imaging(MRI)images is vital for assisting doctors in diagnosing prostate diseases.In recent years,many advanced methods have been applied to prostate segmentation,but due to the variability caused by prostate diseases,automatic segmentation of the prostate presents significant challenges.In this paper,we propose an attention-guided multi-scale feature fusion network(AGMSF-Net)to segment prostate MRI images.We propose an attention mechanism for extracting multi-scale features,and introduce a 3D transformer module to enhance global feature representation by adding it during the transition phase from encoder to decoder.In the decoder stage,a feature fusion module is proposed to obtain global context information.We evaluate our model on MRI images of the prostate acquired from a local hospital.The relative volume difference(RVD)and dice similarity coefficient(DSC)between the results of automatic prostate segmentation and ground truth were 1.21%and 93.68%,respectively.To quantitatively evaluate prostate volume on MRI,which is of significant clinical significance,we propose a unique AGMSF-Net.The essential performance evaluation and validation experiments have demonstrated the effectiveness of our method in automatic prostate segmentation.展开更多
Traditional classification methods for cervical cells heavily rely on manual feature extraction,constraining their versatility due to the intricacies of cytology images.Although deep learning approaches offer remarkab...Traditional classification methods for cervical cells heavily rely on manual feature extraction,constraining their versatility due to the intricacies of cytology images.Although deep learning approaches offer remarkable po-tential,they often sacrifice domain-specific knowledge,particularly the morphological patterns characterizing various cell subtypes during automated feature extraction.To bridge this gap,we introduce a novel hierarchical framework that integrates robust features from color,texture,and morphology with latent representations discovered by an improved attention-based multi-scale local binary convolutional neural networks(MS-LBCNN),designed to facilitate powerful feature extraction mechanism.We enhance the standard 6-class Bethesda system(TBS)classification by incorporating a coarse-to-refine fusion strategy,which optimizes the classification pro-cess.The proposed method is uniquely equipped to manage the complexities present in both individual and clustered cell images.Upon rigorous evaluation across three independent data cohorts,our method consistently surpassed existing state-of-the-art techniques.The experimental results indicated the potential of our method in enhancing the development of automation-aided diagnostic systems,and bolstering both the accuracy and ef-ficiency of cytology screening procedures.展开更多
Recently,deep learning-based image inpainting methods have made great strides in reconstructing damaged regions.However,these methods often struggle to produce satisfactory results when dealing with missing images wit...Recently,deep learning-based image inpainting methods have made great strides in reconstructing damaged regions.However,these methods often struggle to produce satisfactory results when dealing with missing images with large holes,leading to distortions in the structure and blurring of textures.To address these problems,we combine the advantages of transformers and convolutions to propose an image inpainting method that incorporates edge priors and attention mechanisms.The proposed method aims to improve the results of inpainting large holes in images by enhancing the accuracy of structure restoration and the ability to recover texture details.This method divides the inpainting task into two phases:edge prediction and image inpainting.Specifically,in the edge prediction phase,a transformer architecture is designed to combine axial attention with standard self-attention.This design enhances the extraction capability of global structural features and location awareness.It also balances the complexity of self-attention operations,resulting in accurate prediction of the edge structure in the defective region.In the image inpainting phase,a multi-scale fusion attention module is introduced.This module makes full use of multi-level distant features and enhances local pixel continuity,thereby significantly improving the quality of image inpainting.To evaluate the performance of our method.comparative experiments are conducted on several datasets,including CelebA,Places2,and Facade.Quantitative experiments show that our method outperforms the other mainstream methods.Specifically,it improves Peak Signal-to-Noise Ratio(PSNR)and Structure Similarity Index Measure(SSIM)by 1.141~3.234 db and 0.083~0.235,respectively.Moreover,it reduces Learning Perceptual Image Patch Similarity(LPIPS)and Mean Absolute Error(MAE)by 0.0347~0.1753 and 0.0104~0.0402,respectively.Qualitative experiments reveal that our method excels at reconstructing images with complete structural information and clear texture details.Furthermore,our model exhibits impressive performance in terms of the number of parameters,memory cost,and testing time.展开更多
Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules.In the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it cha...Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules.In the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it challenging to collect defective samples.Additionally,the complex surface background of polysilicon cell wafers complicates the accurate identification and localization of defective regions.This paper proposes a novel Lightweight Multiscale Feature Fusion network(LMFF)to address these challenges.The network comprises a feature extraction network,a multi-scale feature fusion module(MFF),and a segmentation network.Specifically,a feature extraction network is proposed to obtain multi-scale feature outputs,and a multi-scale feature fusion module(MFF)is used to fuse multi-scale feature information effectively.In order to capture finer-grained multi-scale information from the fusion features,we propose a multi-scale attention module(MSA)in the segmentation network to enhance the network’s ability for small target detection.Moreover,depthwise separable convolutions are introduced to construct depthwise separable residual blocks(DSR)to reduce the model’s parameter number.Finally,to validate the proposed method’s defect segmentation and localization performance,we constructed three solar cell defect detection datasets:SolarCells,SolarCells-S,and PVEL-S.SolarCells and SolarCells-S are monocrystalline silicon datasets,and PVEL-S is a polycrystalline silicon dataset.Experimental results show that the IOU of our method on these three datasets can reach 68.5%,51.0%,and 92.7%,respectively,and the F1-Score can reach 81.3%,67.5%,and 96.2%,respectively,which surpasses other commonly usedmethods and verifies the effectiveness of our LMFF network.展开更多
A novel dual-branch decoding fusion convolutional neural network model(DDFNet)specifically designed for real-time salient object detection(SOD)on steel surfaces is proposed.DDFNet is based on a standard encoder–decod...A novel dual-branch decoding fusion convolutional neural network model(DDFNet)specifically designed for real-time salient object detection(SOD)on steel surfaces is proposed.DDFNet is based on a standard encoder–decoder architecture.DDFNet integrates three key innovations:first,we introduce a novel,lightweight multi-scale progressive aggregation residual network that effectively suppresses background interference and refines defect details,enabling efficient salient feature extraction.Then,we propose an innovative dual-branch decoding fusion structure,comprising the refined defect representation branch and the enhanced defect representation branch,which enhance accuracy in defect region identification and feature representation.Additionally,to further improve the detection of small and complex defects,we incorporate a multi-scale attention fusion module.Experimental results on the public ESDIs-SOD dataset show that DDFNet,with only 3.69 million parameters,achieves detection performance comparable to current state-of-the-art models,demonstrating its potential for real-time industrial applications.Furthermore,our DDFNet-L variant consistently outperforms leading methods in detection performance.The code is available at https://github.com/13140W/DDFNet.展开更多
At present,research on multi-label image classification mainly focuses on exploring the correlation between labels to improve the classification accuracy of multi-label images.However,in existing methods,label correla...At present,research on multi-label image classification mainly focuses on exploring the correlation between labels to improve the classification accuracy of multi-label images.However,in existing methods,label correlation is calculated based on the statistical information of the data.This label correlation is global and depends on the dataset,not suitable for all samples.In the process of extracting image features,the characteristic information of small objects in the image is easily lost,resulting in a low classification accuracy of small objects.To this end,this paper proposes a multi-label image classification model based on multiscale fusion and adaptive label correlation.The main idea is:first,the feature maps of multiple scales are fused to enhance the feature information of small objects.Semantic guidance decomposes the fusion feature map into feature vectors of each category,then adaptively mines the correlation between categories in the image through the self-attention mechanism of graph attention network,and obtains feature vectors containing category-related information for the final classification.The mean average precision of the model on the two public datasets of VOC 2007 and MS COCO 2014 reached 95.6% and 83.6%,respectively,and most of the indicators are better than those of the existing latest methods.展开更多
Image demoiréing is a complex image-restoration task because of the color and shape variations of moirépatterns.With the development of mobile devices,mobile phones can now be used to capture images at multi...Image demoiréing is a complex image-restoration task because of the color and shape variations of moirépatterns.With the development of mobile devices,mobile phones can now be used to capture images at multiple resolutions.This difficulty increases when attempting to remove moiréfrom both low-and high-resolution images,as different resolutions make it challenging for existing methods to match the scales and textures of moiré.To solve these problems,we built a mixed attention residual module(MARM)by combining multi-scale feature extraction and mixed attention methods.Based on MARM,we propose a multi-scale adaptive mixed attention network(MA2Net)that can adapt to input images of different sizes and remove moiréof various shapes.Our model achieved the best results on four public datasets with resolutions ranging from 256×256 to 4k.Extensive experiments demonstrated the effectiveness of our model,which outperformed state-of-the-art methods by a large margin.We also conducted experiments on image deraining to validate the effectiveness of our model in other image-restoration tasks,and MA2Net achieved state-of-the-art performance on the Rain200H dataset.展开更多
With the development of smart agriculture,accurately identifying crop diseases through visual recognition techniques instead of by eye has been a significant challenge.This study focused on apple leaf disease,which is...With the development of smart agriculture,accurately identifying crop diseases through visual recognition techniques instead of by eye has been a significant challenge.This study focused on apple leaf disease,which is closely related to the final yield of apples.A multiscale fusion dense network combined with an efficient multiscale attention(EMA)mechanism called Incept_EMA_DenseNet was developed to better identify eight complex apple leaf disease images.Incept_EMA_DenseNet consists of three crucial parts:the inception module,which substituted the convolution layer with multiscale fusion methods in the shallow feature extraction layer;the EMA mechanism,which is used for obtaining appropriate weights of different dense blocks;and the improved DenseNet based on DenseNet_121.Specifically,to find appropriate multiscale fusion methods,the residual module and inception module were compared to determine the performance of each technique,and Incept_EMA_DenseNet achieved an accuracy of 95.38%.Second,this work used three attention mechanisms,and the efficient multiscale attention mechanism obtained the best performance.Third,the convolution layers and bottlenecks were modified without performance degradation,reducing half of the computational load compared with the original models.Incept_EMA_DenseNet,as proposed in this paper,has an accuracy of 96.76%,being 2.93%,3.44%,and 4.16%better than Resnet50,DenseNet_121 and GoogLeNet,respectively,proved to be reliable and beneficial,and can effectively and conveniently assist apple growers with leaf disease identification in the field.展开更多
Tea shoot segmentation is crucial for the automation of high-quality tea plucking.However,accurate segmentation of tea shoots in unstructured and complex environments presents significant challenges due to the small s...Tea shoot segmentation is crucial for the automation of high-quality tea plucking.However,accurate segmentation of tea shoots in unstructured and complex environments presents significant challenges due to the small size of the targets and the similarity in color between the shoots and their background.To address these challenges and achieve accurate recognition of tea shoots in complex settings,an advanced tea shoot segmentation network model is proposed based on You Only Look Once version 8 segmentation(YOLOv8-seg)network model.Firstly,to enhance the model’s segmentation capability for small targets,this study designed a feature fusion network that incorporates shallow,large-scale features extracted by the backbone network.Subsequently,the features extracted at different scales by the backbone network are fused to obtain both global and local features,thereby enhancing the overall information representation capability of the features.Furthermore,the Efficient Channel Attention mechanism was integrated into the feature fusion process and combined with a reparameterization technique to refine and improve the efficiency of the fusion process.Finally,Wise-IoU with a dynamic non-monotonic aggregation mechanism was employed to assign varying gradient gains to anchor boxes of differing qualities.Experimental results demonstrate that the improved network model increases the AP50 of box and mask by 4.33%and 4.55%,respectively,while maintaining a smaller parameter count and reduced computational demand.Compared to other classical segmentation algorithms models,the proposed model excels in tea shoot segmentation.Overall,the advancements proposed in this study effectively segment tea shoots in complex environments,offering significant theoretical and practical contributions to the automated plucking of high-quality tea.展开更多
In recent years,convolutional neural networks(CNN)and Transformer architectures have made significant progress in the field of remote sensing(RS)change detection(CD).Most of the existing methods directly stack multipl...In recent years,convolutional neural networks(CNN)and Transformer architectures have made significant progress in the field of remote sensing(RS)change detection(CD).Most of the existing methods directly stack multiple layers of Transformer blocks,which achieves considerable improvement in capturing variations,but at a rather high computational cost.We propose a channel-Efficient Change Detection Network(CE-CDNet)to address the problems of high computational cost and imbalanced detection accuracy in remote sensing building change detection.The adaptive multi-scale feature fusion module(CAMSF)and lightweight Transformer decoder(LTD)are introduced to improve the change detection effect.The CAMSF module can adaptively fuse multi-scale features to improve the model’s ability to detect building changes in complex scenes.In addition,the LTD module reduces computational costs and maintains high detection accuracy through an optimized self-attention mechanism and dimensionality reduction operation.Experimental test results on three commonly used remote sensing building change detection data sets show that CE-CDNet can reduce a certain amount of computational overhead while maintaining detection accuracy comparable to existing mainstream models,showing good performance advantages.展开更多
Grasp detection plays a critical role for robot manipulation.Mainstream pixel-wise grasp detection networks with encoder-decoder structure receive much attention due to good accuracy and efficiency.However,they usuall...Grasp detection plays a critical role for robot manipulation.Mainstream pixel-wise grasp detection networks with encoder-decoder structure receive much attention due to good accuracy and efficiency.However,they usually transmit the high-level feature in the encoder to the decoder,and low-level features are neglected.It is noted that low-level features contain abundant detail information,and how to fully exploit low-level features remains unsolved.Meanwhile,the channel information in high-level feature is also not well mined.Inevitably,the performance of grasp detection is degraded.To solve these problems,we propose a grasp detection network with hierarchical multi-scale feature fusion and inverted shuffle residual.Both low-level and high-level features in the encoder are firstly fused by the designed skip connections with attention module,and the fused information is then propagated to corresponding layers of the decoder for in-depth feature fusion.Such a hierarchical fusion guarantees the quality of grasp prediction.Furthermore,an inverted shuffle residual module is created,where the high-level feature from encoder is split in channel and the resultant split features are processed in their respective branches.By such differentiation processing,more high-dimensional channel information is kept,which enhances the representation ability of the network.Besides,an information enhancement module is added before the encoder to reinforce input information.The proposed method attains 98.9%and 97.8%in image-wise and object-wise accuracy on the Cornell grasping dataset,respectively,and the experimental results verify the effectiveness of the method.展开更多
Fine-grained visual classification(FGVC)is a very challenging task due to distinguishing subcategories under the same super-category.Recent works mainly localize discriminative image regions and capture subtle inter-c...Fine-grained visual classification(FGVC)is a very challenging task due to distinguishing subcategories under the same super-category.Recent works mainly localize discriminative image regions and capture subtle inter-class differences by utilizing attention-based methods.However,at the same layer,most attention-based works only consider large-scale attention blocks with the same size as feature maps,and they ignore small-scale attention blocks that are smaller than feature maps.To distinguish subcategories,it is important to exploit small local regions.In this work,a novel multi-scale attention network(MSANet)is proposed to capture large and small regions at the same layer in fine-grained visual classification.Specifically,a novel multi-scale attention layer(MSAL)is proposed,which generates multiple groups in each feature maps to capture different-scale discriminative regions.The groups based on large-scale regions can exploit global features and the groups based on the small-scale regions can extract local subtle features.Then,a simple feature fusion strategy is utilized to fully integrate global features and local subtle features to mine information that are more conducive to FGVC.Comprehensive experiments in Caltech-UCSD Birds-200-2011(CUB),FGVC-Aircraft(AIR)and Stanford Cars(Cars)datasets show that our method achieves the competitive performances,which demonstrate its effectiveness.展开更多
[Objective]Leaf diseases significantly affect both the yield and quality of tea throughout the year.To address the issue of inadequate segmentation finesse in the current tea spot segmentation models,a novel diagnosis...[Objective]Leaf diseases significantly affect both the yield and quality of tea throughout the year.To address the issue of inadequate segmentation finesse in the current tea spot segmentation models,a novel diagnosis of the severity of tea spots was proposed in this research,designated as MDC-U-Net3+,to enhance segmentation accuracy on the base framework of U-Net3+.[Methods]Multi-scale feature fusion module(MSFFM)was incorporated into the backbone network of U-Net3+to obtain feature information across multiple receptive fields of diseased spots,thereby reducing the loss of features within the encoder.Dual multi-scale attention(DMSA)was incorporated into the skip connection process to mitigate the segmentation boundary ambiguity issue.This integration facilitates the comprehensive fusion of fine-grained and coarse-grained semantic information at full scale.Furthermore,the segmented mask image was subjected to conditional random fields(CRF)to enhance the optimization of the segmentation results[Results and Discussions]The improved model MDC-U-Net3+achieved a mean pixel accuracy(mPA)of 94.92%,accompanied by a mean Intersection over Union(mIoU)ratio of 90.9%.When compared to the mPA and mIoU of U-Net3+,MDC-U-Net3+model showed improvements of 1.85 and 2.12 percentage points,respectively.These results illustrated a more effective segmentation performance than that achieved by other classical semantic segmentation models.[Conclusions]The methodology presented herein could provide data support for automated disease detection and precise medication,consequently reducing the losses associated with tea diseases.展开更多
基金supported by the Natural Science Foundation of the Anhui Higher Education Institutions of China(Grant Nos.2023AH040149 and 2024AH051915)the Anhui Provincial Natural Science Foundation(Grant No.2208085MF168)+1 种基金the Science and Technology Innovation Tackle Plan Project of Maanshan(Grant No.2024RGZN001)the Scientific Research Fund Project of Anhui Medical University(Grant No.2023xkj122).
文摘Convolutional neural networks(CNNs)-based medical image segmentation technologies have been widely used in medical image segmentation because of their strong representation and generalization abilities.However,due to the inability to effectively capture global information from images,CNNs can easily lead to loss of contours and textures in segmentation results.Notice that the transformer model can effectively capture the properties of long-range dependencies in the image,and furthermore,combining the CNN and the transformer can effectively extract local details and global contextual features of the image.Motivated by this,we propose a multi-branch and multi-scale attention network(M2ANet)for medical image segmentation,whose architecture consists of three components.Specifically,in the first component,we construct an adaptive multi-branch patch module for parallel extraction of image features to reduce information loss caused by downsampling.In the second component,we apply residual block to the well-known convolutional block attention module to enhance the network’s ability to recognize important features of images and alleviate the phenomenon of gradient vanishing.In the third component,we design a multi-scale feature fusion module,in which we adopt adaptive average pooling and position encoding to enhance contextual features,and then multi-head attention is introduced to further enrich feature representation.Finally,we validate the effectiveness and feasibility of the proposed M2ANet method through comparative experiments on four benchmark medical image segmentation datasets,particularly in the context of preserving contours and textures.
文摘With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.
基金supported by National Natural Science Foundation of China(No.61862037)Lanzhou Jiaotong University Tianyou Innovation Team Project(No.TY202002)。
文摘To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network model is proposed by fusing multi-scale feature information.Firstly,a multi-scale feature extraction module is designed to obtain multi-scale information on feature images by using different scales of convolution kernels.Meanwhile,the channel attention mechanism is used to increase the global information acquisition of the network.Secondly,the feature images processed by the multi-scale feature extraction module are fused with the deep feature images through short links to guide the full learning of the network,thus reducing the loss of texture details of the deep network feature images,and improving network generalization ability and recognition accuracy.Finally,the validity of the MSFResNet model is verified using public datasets and applied to wild mushroom identification.Experimental results show that compared with ResNeXt50 network model,the accuracy of the MSFResNet model is improved by 6.01%on the FGVC-Aircraft common dataset.It achieves 99.13%classification accuracy on the wild mushroom dataset,which is 0.47%higher than ResNeXt50.Furthermore,the experimental results of the thermal map show that the MSFResNet model significantly reduces the interference of background information,making the network focus on the location of the main body of wild mushroom,which can effectively improve the accuracy of wild mushroom identification.
基金supported by the National Natural Science Foundation of China(No.62376287)the International Science and Technology Innovation Joint Base of Machine Vision and Medical Image Processing in Hunan Province(2021CB1013)the Natural Science Foundation of Hunan Province(Nos.2022JJ30762,2023JJ70016).
文摘Globally,diabetic retinopathy(DR)is the primary cause of blindness,affecting millions of people worldwide.This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective treatment.Deep learning-based automated diagnosis for diabetic retinopathy can facilitate early detection and treatment.However,traditional deep learning models that focus on local views often learn feature representations that are less discriminative at the semantic level.On the other hand,models that focus on global semantic-level information might overlook critical,subtle local pathological features.To address this issue,we propose an adaptive multi-scale feature fusion network called(AMSFuse),which can adaptively combine multi-scale global and local features without compromising their individual representation.Specifically,our model incorporates global features for extracting high-level contextual information from retinal images.Concurrently,local features capture fine-grained details,such as microaneurysms,hemorrhages,and exudates,which are critical for DR diagnosis.These global and local features are adaptively fused using a fusion block,followed by an Integrated Attention Mechanism(IAM)that refines the fused features by emphasizing relevant regions,thereby enhancing classification accuracy for DR classification.Our model achieves 86.3%accuracy on the APTOS dataset and 96.6%RFMiD,both of which are comparable to state-of-the-art methods.
基金the National Natural Science Foundation of China(Nos.61775139,62072126,61772164,and 61872242)。
文摘Human pose estimation has received much attention from the research community because of its wide range of applications.However,current research for pose estimation is usually complex and computationally intensive,especially the feature loss problems in the feature fusion process.To address the above problems,we propose a lightweight human pose estimation network based on multi-attention mechanism(LMANet).In our method,network parameters can be significantly reduced by lightweighting the bottleneck blocks with depth-wise separable convolution on the high-resolution networks.After that,we also introduce a multi-attention mechanism to improve the model prediction accuracy,and the channel attention module is added in the initial stage of the network to enhance the local cross-channel information interaction.More importantly,we inject spatial crossawareness module in the multi-scale feature fusion stage to reduce the spatial information loss during feature extraction.Extensive experiments on COCO2017 dataset and MPII dataset show that LMANet can guarantee a higher prediction accuracy with fewer network parameters and computational effort.Compared with the highresolution network HRNet,the number of parameters and the computational complexity of the network are reduced by 67%and 73%,respectively.
基金This work was supported in part by the National Natural Science Foundation of China(Grant#:82260362)in part by the National Key R&D Program of China(Grant#:2021ZD0111000)+1 种基金in part by the Key R&D Project of Hainan Province(Grant#:ZDYF2021SHFZ243)in part by the Major Science and Technology Project of Haikou(Grant#:2020-009).
文摘The precise and automatic segmentation of prostate magnetic resonance imaging(MRI)images is vital for assisting doctors in diagnosing prostate diseases.In recent years,many advanced methods have been applied to prostate segmentation,but due to the variability caused by prostate diseases,automatic segmentation of the prostate presents significant challenges.In this paper,we propose an attention-guided multi-scale feature fusion network(AGMSF-Net)to segment prostate MRI images.We propose an attention mechanism for extracting multi-scale features,and introduce a 3D transformer module to enhance global feature representation by adding it during the transition phase from encoder to decoder.In the decoder stage,a feature fusion module is proposed to obtain global context information.We evaluate our model on MRI images of the prostate acquired from a local hospital.The relative volume difference(RVD)and dice similarity coefficient(DSC)between the results of automatic prostate segmentation and ground truth were 1.21%and 93.68%,respectively.To quantitatively evaluate prostate volume on MRI,which is of significant clinical significance,we propose a unique AGMSF-Net.The essential performance evaluation and validation experiments have demonstrated the effectiveness of our method in automatic prostate segmentation.
基金supported by the Beijing Capital Health Development Research Project[Grant no.2024-2-1031]the Beijing Municipal Natural Science Foundation[Grant no.7192105].
文摘Traditional classification methods for cervical cells heavily rely on manual feature extraction,constraining their versatility due to the intricacies of cytology images.Although deep learning approaches offer remarkable po-tential,they often sacrifice domain-specific knowledge,particularly the morphological patterns characterizing various cell subtypes during automated feature extraction.To bridge this gap,we introduce a novel hierarchical framework that integrates robust features from color,texture,and morphology with latent representations discovered by an improved attention-based multi-scale local binary convolutional neural networks(MS-LBCNN),designed to facilitate powerful feature extraction mechanism.We enhance the standard 6-class Bethesda system(TBS)classification by incorporating a coarse-to-refine fusion strategy,which optimizes the classification pro-cess.The proposed method is uniquely equipped to manage the complexities present in both individual and clustered cell images.Upon rigorous evaluation across three independent data cohorts,our method consistently surpassed existing state-of-the-art techniques.The experimental results indicated the potential of our method in enhancing the development of automation-aided diagnostic systems,and bolstering both the accuracy and ef-ficiency of cytology screening procedures.
基金supported in part by the National Natural Science Foundation of China under Grant 62062061/in part by the Major Project Cultivation Fund of Xizang Minzu University under Grant 324112300447.
文摘Recently,deep learning-based image inpainting methods have made great strides in reconstructing damaged regions.However,these methods often struggle to produce satisfactory results when dealing with missing images with large holes,leading to distortions in the structure and blurring of textures.To address these problems,we combine the advantages of transformers and convolutions to propose an image inpainting method that incorporates edge priors and attention mechanisms.The proposed method aims to improve the results of inpainting large holes in images by enhancing the accuracy of structure restoration and the ability to recover texture details.This method divides the inpainting task into two phases:edge prediction and image inpainting.Specifically,in the edge prediction phase,a transformer architecture is designed to combine axial attention with standard self-attention.This design enhances the extraction capability of global structural features and location awareness.It also balances the complexity of self-attention operations,resulting in accurate prediction of the edge structure in the defective region.In the image inpainting phase,a multi-scale fusion attention module is introduced.This module makes full use of multi-level distant features and enhances local pixel continuity,thereby significantly improving the quality of image inpainting.To evaluate the performance of our method.comparative experiments are conducted on several datasets,including CelebA,Places2,and Facade.Quantitative experiments show that our method outperforms the other mainstream methods.Specifically,it improves Peak Signal-to-Noise Ratio(PSNR)and Structure Similarity Index Measure(SSIM)by 1.141~3.234 db and 0.083~0.235,respectively.Moreover,it reduces Learning Perceptual Image Patch Similarity(LPIPS)and Mean Absolute Error(MAE)by 0.0347~0.1753 and 0.0104~0.0402,respectively.Qualitative experiments reveal that our method excels at reconstructing images with complete structural information and clear texture details.Furthermore,our model exhibits impressive performance in terms of the number of parameters,memory cost,and testing time.
基金supported in part by the National Natural Science Foundation of China under Grants 62463002,62062021 and 62473033in part by the Guiyang Scientific Plan Project[2023]48–11,in part by QKHZYD[2023]010 Guizhou Province Science and Technology Innovation Base Construction Project“Key Laboratory Construction of Intelligent Mountain Agricultural Equipment”.
文摘Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules.In the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it challenging to collect defective samples.Additionally,the complex surface background of polysilicon cell wafers complicates the accurate identification and localization of defective regions.This paper proposes a novel Lightweight Multiscale Feature Fusion network(LMFF)to address these challenges.The network comprises a feature extraction network,a multi-scale feature fusion module(MFF),and a segmentation network.Specifically,a feature extraction network is proposed to obtain multi-scale feature outputs,and a multi-scale feature fusion module(MFF)is used to fuse multi-scale feature information effectively.In order to capture finer-grained multi-scale information from the fusion features,we propose a multi-scale attention module(MSA)in the segmentation network to enhance the network’s ability for small target detection.Moreover,depthwise separable convolutions are introduced to construct depthwise separable residual blocks(DSR)to reduce the model’s parameter number.Finally,to validate the proposed method’s defect segmentation and localization performance,we constructed three solar cell defect detection datasets:SolarCells,SolarCells-S,and PVEL-S.SolarCells and SolarCells-S are monocrystalline silicon datasets,and PVEL-S is a polycrystalline silicon dataset.Experimental results show that the IOU of our method on these three datasets can reach 68.5%,51.0%,and 92.7%,respectively,and the F1-Score can reach 81.3%,67.5%,and 96.2%,respectively,which surpasses other commonly usedmethods and verifies the effectiveness of our LMFF network.
基金supported in part by the National Key R&D Program of China(Grant No.2023YFB3307604)the Shanxi Province Basic Research Program Youth Science Research Project(Grant Nos.202303021212054 and 202303021212046)+3 种基金the Key Projects Supported by Hebei Natural Science Foundation(Grant No.E2024203125)the National Science Foundation of China(Grant No.52105391)the Hebei Provincial Science and Technology Major Project(Grant No.23280101Z)the National Key Laboratory of Metal Forming Technology and Heavy Equipment Open Fund(Grant No.S2308100.W17).
文摘A novel dual-branch decoding fusion convolutional neural network model(DDFNet)specifically designed for real-time salient object detection(SOD)on steel surfaces is proposed.DDFNet is based on a standard encoder–decoder architecture.DDFNet integrates three key innovations:first,we introduce a novel,lightweight multi-scale progressive aggregation residual network that effectively suppresses background interference and refines defect details,enabling efficient salient feature extraction.Then,we propose an innovative dual-branch decoding fusion structure,comprising the refined defect representation branch and the enhanced defect representation branch,which enhance accuracy in defect region identification and feature representation.Additionally,to further improve the detection of small and complex defects,we incorporate a multi-scale attention fusion module.Experimental results on the public ESDIs-SOD dataset show that DDFNet,with only 3.69 million parameters,achieves detection performance comparable to current state-of-the-art models,demonstrating its potential for real-time industrial applications.Furthermore,our DDFNet-L variant consistently outperforms leading methods in detection performance.The code is available at https://github.com/13140W/DDFNet.
基金the National Natural Science Foundation of China(Nos.62167005 and 61966018)the Key Research Projects of Jiangxi Provincial Department of Education(No.GJJ200302)。
文摘At present,research on multi-label image classification mainly focuses on exploring the correlation between labels to improve the classification accuracy of multi-label images.However,in existing methods,label correlation is calculated based on the statistical information of the data.This label correlation is global and depends on the dataset,not suitable for all samples.In the process of extracting image features,the characteristic information of small objects in the image is easily lost,resulting in a low classification accuracy of small objects.To this end,this paper proposes a multi-label image classification model based on multiscale fusion and adaptive label correlation.The main idea is:first,the feature maps of multiple scales are fused to enhance the feature information of small objects.Semantic guidance decomposes the fusion feature map into feature vectors of each category,then adaptively mines the correlation between categories in the image through the self-attention mechanism of graph attention network,and obtains feature vectors containing category-related information for the final classification.The mean average precision of the model on the two public datasets of VOC 2007 and MS COCO 2014 reached 95.6% and 83.6%,respectively,and most of the indicators are better than those of the existing latest methods.
基金National Natural Science Foundation of China under Grant No.12371384Beijing Natural Science Foundation under Grant No.Z190004Fundamental Research Funds for the Central Universities.
文摘Image demoiréing is a complex image-restoration task because of the color and shape variations of moirépatterns.With the development of mobile devices,mobile phones can now be used to capture images at multiple resolutions.This difficulty increases when attempting to remove moiréfrom both low-and high-resolution images,as different resolutions make it challenging for existing methods to match the scales and textures of moiré.To solve these problems,we built a mixed attention residual module(MARM)by combining multi-scale feature extraction and mixed attention methods.Based on MARM,we propose a multi-scale adaptive mixed attention network(MA2Net)that can adapt to input images of different sizes and remove moiréof various shapes.Our model achieved the best results on four public datasets with resolutions ranging from 256×256 to 4k.Extensive experiments demonstrated the effectiveness of our model,which outperformed state-of-the-art methods by a large margin.We also conducted experiments on image deraining to validate the effectiveness of our model in other image-restoration tasks,and MA2Net achieved state-of-the-art performance on the Rain200H dataset.
基金fully supported by the National Natural Science Foundation of China(52072412)。
文摘With the development of smart agriculture,accurately identifying crop diseases through visual recognition techniques instead of by eye has been a significant challenge.This study focused on apple leaf disease,which is closely related to the final yield of apples.A multiscale fusion dense network combined with an efficient multiscale attention(EMA)mechanism called Incept_EMA_DenseNet was developed to better identify eight complex apple leaf disease images.Incept_EMA_DenseNet consists of three crucial parts:the inception module,which substituted the convolution layer with multiscale fusion methods in the shallow feature extraction layer;the EMA mechanism,which is used for obtaining appropriate weights of different dense blocks;and the improved DenseNet based on DenseNet_121.Specifically,to find appropriate multiscale fusion methods,the residual module and inception module were compared to determine the performance of each technique,and Incept_EMA_DenseNet achieved an accuracy of 95.38%.Second,this work used three attention mechanisms,and the efficient multiscale attention mechanism obtained the best performance.Third,the convolution layers and bottlenecks were modified without performance degradation,reducing half of the computational load compared with the original models.Incept_EMA_DenseNet,as proposed in this paper,has an accuracy of 96.76%,being 2.93%,3.44%,and 4.16%better than Resnet50,DenseNet_121 and GoogLeNet,respectively,proved to be reliable and beneficial,and can effectively and conveniently assist apple growers with leaf disease identification in the field.
基金supported by the National Natural Science Foundation of China(Grant No.52305289,Grant No.U23A20175)the Open Fund of State Key Laboratory of Tea Plant Biology and Utilization(Grant No.NKLTOF20240103)the earmarked fund for CARS-19 and the Open Project Program of Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry,Wuyi University.
文摘Tea shoot segmentation is crucial for the automation of high-quality tea plucking.However,accurate segmentation of tea shoots in unstructured and complex environments presents significant challenges due to the small size of the targets and the similarity in color between the shoots and their background.To address these challenges and achieve accurate recognition of tea shoots in complex settings,an advanced tea shoot segmentation network model is proposed based on You Only Look Once version 8 segmentation(YOLOv8-seg)network model.Firstly,to enhance the model’s segmentation capability for small targets,this study designed a feature fusion network that incorporates shallow,large-scale features extracted by the backbone network.Subsequently,the features extracted at different scales by the backbone network are fused to obtain both global and local features,thereby enhancing the overall information representation capability of the features.Furthermore,the Efficient Channel Attention mechanism was integrated into the feature fusion process and combined with a reparameterization technique to refine and improve the efficiency of the fusion process.Finally,Wise-IoU with a dynamic non-monotonic aggregation mechanism was employed to assign varying gradient gains to anchor boxes of differing qualities.Experimental results demonstrate that the improved network model increases the AP50 of box and mask by 4.33%and 4.55%,respectively,while maintaining a smaller parameter count and reduced computational demand.Compared to other classical segmentation algorithms models,the proposed model excels in tea shoot segmentation.Overall,the advancements proposed in this study effectively segment tea shoots in complex environments,offering significant theoretical and practical contributions to the automated plucking of high-quality tea.
基金supported by Henan Province Key R&D Project(241111210400)Henan Provincial Science and Technology Research Project(242102211007 and 242102211020)+1 种基金Jiangsu Science and Technology Programme-General Programme(BK20221260)Science and Technology Innovation Project of Zhengzhou University of Light Industry(23XNKJTD0205).
文摘In recent years,convolutional neural networks(CNN)and Transformer architectures have made significant progress in the field of remote sensing(RS)change detection(CD).Most of the existing methods directly stack multiple layers of Transformer blocks,which achieves considerable improvement in capturing variations,but at a rather high computational cost.We propose a channel-Efficient Change Detection Network(CE-CDNet)to address the problems of high computational cost and imbalanced detection accuracy in remote sensing building change detection.The adaptive multi-scale feature fusion module(CAMSF)and lightweight Transformer decoder(LTD)are introduced to improve the change detection effect.The CAMSF module can adaptively fuse multi-scale features to improve the model’s ability to detect building changes in complex scenes.In addition,the LTD module reduces computational costs and maintains high detection accuracy through an optimized self-attention mechanism and dimensionality reduction operation.Experimental test results on three commonly used remote sensing building change detection data sets show that CE-CDNet can reduce a certain amount of computational overhead while maintaining detection accuracy comparable to existing mainstream models,showing good performance advantages.
基金This work was supported by the National Natural Science Foundation of China(Nos.62073322 and 61633020)the CIE-Tencent Robotics X Rhino-Bird Focused Research Program(No.2022-07)the Beijing Natural Science Foundation(No.2022MQ05).
文摘Grasp detection plays a critical role for robot manipulation.Mainstream pixel-wise grasp detection networks with encoder-decoder structure receive much attention due to good accuracy and efficiency.However,they usually transmit the high-level feature in the encoder to the decoder,and low-level features are neglected.It is noted that low-level features contain abundant detail information,and how to fully exploit low-level features remains unsolved.Meanwhile,the channel information in high-level feature is also not well mined.Inevitably,the performance of grasp detection is degraded.To solve these problems,we propose a grasp detection network with hierarchical multi-scale feature fusion and inverted shuffle residual.Both low-level and high-level features in the encoder are firstly fused by the designed skip connections with attention module,and the fused information is then propagated to corresponding layers of the decoder for in-depth feature fusion.Such a hierarchical fusion guarantees the quality of grasp prediction.Furthermore,an inverted shuffle residual module is created,where the high-level feature from encoder is split in channel and the resultant split features are processed in their respective branches.By such differentiation processing,more high-dimensional channel information is kept,which enhances the representation ability of the network.Besides,an information enhancement module is added before the encoder to reinforce input information.The proposed method attains 98.9%and 97.8%in image-wise and object-wise accuracy on the Cornell grasping dataset,respectively,and the experimental results verify the effectiveness of the method.
基金jointly supported by the National Science and Technology Major Project(2022ZD0117103)the National Natural Science Foundations of China(62272364)+2 种基金the provincial Key Research and Development Program of Shaanxi(2024GH-ZDXM-47)the Research Project on Higher Education Teaching Reform of Shaanxi Province(23JG003)the Natural Science Basic Research Program of Shaanxi(2024JC-YBQN0639).
文摘Fine-grained visual classification(FGVC)is a very challenging task due to distinguishing subcategories under the same super-category.Recent works mainly localize discriminative image regions and capture subtle inter-class differences by utilizing attention-based methods.However,at the same layer,most attention-based works only consider large-scale attention blocks with the same size as feature maps,and they ignore small-scale attention blocks that are smaller than feature maps.To distinguish subcategories,it is important to exploit small local regions.In this work,a novel multi-scale attention network(MSANet)is proposed to capture large and small regions at the same layer in fine-grained visual classification.Specifically,a novel multi-scale attention layer(MSAL)is proposed,which generates multiple groups in each feature maps to capture different-scale discriminative regions.The groups based on large-scale regions can exploit global features and the groups based on the small-scale regions can extract local subtle features.Then,a simple feature fusion strategy is utilized to fully integrate global features and local subtle features to mine information that are more conducive to FGVC.Comprehensive experiments in Caltech-UCSD Birds-200-2011(CUB),FGVC-Aircraft(AIR)and Stanford Cars(Cars)datasets show that our method achieves the competitive performances,which demonstrate its effectiveness.
文摘[Objective]Leaf diseases significantly affect both the yield and quality of tea throughout the year.To address the issue of inadequate segmentation finesse in the current tea spot segmentation models,a novel diagnosis of the severity of tea spots was proposed in this research,designated as MDC-U-Net3+,to enhance segmentation accuracy on the base framework of U-Net3+.[Methods]Multi-scale feature fusion module(MSFFM)was incorporated into the backbone network of U-Net3+to obtain feature information across multiple receptive fields of diseased spots,thereby reducing the loss of features within the encoder.Dual multi-scale attention(DMSA)was incorporated into the skip connection process to mitigate the segmentation boundary ambiguity issue.This integration facilitates the comprehensive fusion of fine-grained and coarse-grained semantic information at full scale.Furthermore,the segmented mask image was subjected to conditional random fields(CRF)to enhance the optimization of the segmentation results[Results and Discussions]The improved model MDC-U-Net3+achieved a mean pixel accuracy(mPA)of 94.92%,accompanied by a mean Intersection over Union(mIoU)ratio of 90.9%.When compared to the mPA and mIoU of U-Net3+,MDC-U-Net3+model showed improvements of 1.85 and 2.12 percentage points,respectively.These results illustrated a more effective segmentation performance than that achieved by other classical semantic segmentation models.[Conclusions]The methodology presented herein could provide data support for automated disease detection and precise medication,consequently reducing the losses associated with tea diseases.
