In order to solve the problem that existing multivariate grey incidence models cannot be applied to time series on different scales, a new model is proposed based on spatial pyramid pooling.Firstly, local features of ...In order to solve the problem that existing multivariate grey incidence models cannot be applied to time series on different scales, a new model is proposed based on spatial pyramid pooling.Firstly, local features of multivariate time series on different scales are pooled and aggregated by spatial pyramid pooling to construct n levels feature pooling matrices on the same scale. Secondly,Deng's multivariate grey incidence model is introduced to measure the degree of incidence between feature pooling matrices at each level. Thirdly, grey incidence degrees at each level are integrated into a global incidence degree. Finally, the performance of the proposed model is verified on two data sets compared with a variety of algorithms. The results illustrate that the proposed model is more effective and efficient than other similarity measure algorithms.展开更多
Sinus floor elevation with a lateral window approach requires bone graft(BG)to ensure sufficient bone mass,and it is necessary to measure and analyse the BG region for follow-up of postoperative patients.However,the B...Sinus floor elevation with a lateral window approach requires bone graft(BG)to ensure sufficient bone mass,and it is necessary to measure and analyse the BG region for follow-up of postoperative patients.However,the BG region from cone-beam computed tomography(CBCT)images is connected to the margin of the maxillary sinus,and its boundary is blurred.Common segmentation methods are usually performed manually by experienced doctors,and are complicated by challenges such as low efficiency and low precision.In this study,an auto-segmentation approach was applied to the BG region within the maxillary sinus based on an atrous spatial pyramid convolution(ASPC)network.The ASPC module was adopted using residual connections to compose multiple atrous convolutions,which could extract more features on multiple scales.Subsequently,a segmentation network of the BG region with multiple ASPC modules was established,which effectively improved the segmentation performance.Although the training data were insufficient,our networks still achieved good auto-segmentation results,with a dice coefficient(Dice)of 87.13%,an Intersection over Union(Iou)of 78.01%,and a sensitivity of 95.02%.Compared with other methods,our method achieved a better segmentation effect,and effectively reduced the misjudgement of segmentation.Our method can thus be used to implement automatic segmentation of the BG region and improve doctors’work efficiency,which is of great importance for developing preliminary studies on the measurement of postoperative BG within the maxillary sinus.展开更多
Face recognition is a big challenge in the research field with a lot of problems like misalignment,illumination changes,pose variations,occlusion,and expressions.Providing a single solution to solve all these problems...Face recognition is a big challenge in the research field with a lot of problems like misalignment,illumination changes,pose variations,occlusion,and expressions.Providing a single solution to solve all these problems at a time is a challenging task.We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching.The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching andmax-pooling.Finally,the input image is recognized using a robust kernel representation method using extracted features.The qualitative and quantitative analysis of the proposed method is carried on benchmark image datasets.Experimental results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR,ORL,LFW,and FERET face recognition datasets.展开更多
Purpose:The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images.Furthermore,this work presents an improved variant of the Tiny YOLOv7 model developed specifically fo...Purpose:The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images.Furthermore,this work presents an improved variant of the Tiny YOLOv7 model developed specifically for eye disease detection.The model proposed in this work is a highly useful tool for the development of applications for autonomous detection of eye diseases in fundus images that can help and assist ophthalmologists.Design/methodology/approach:The approach adopted to carry out this work is twofold.Firstly,a richly annotated dataset consisting of eye disease classes,namely,cataract,glaucoma,retinal disease and normal eye,was created.Secondly,an improved variant of the Tiny YOLOv7 model was developed and proposed as EYE-YOLO.The proposed EYE-YOLO model has been developed by integrating multi-spatial pyramid pooling in the feature extraction network and Focal-EIOU loss in the detection network of the Tiny YOLOv7 model.Moreover,at run time,the mosaic augmentation strategy has been utilized with the proposed model to achieve benchmark results.Further,evaluations have been carried out for performance metrics,namely,precision,recall,F1 Score,average precision(AP)and mean average precision(mAP).Findings:The proposed EYE-YOLO achieved 28%higher precision,18%higher recall,24%higher F1 Score and 30.81%higher mAP than the Tiny YOLOv7 model.Moreover,in terms of AP for each class of the employed dataset,it achieved 9.74%higher AP for cataract,27.73%higher AP for glaucoma,72.50%higher AP for retina disease and 13.26%higher AP for normal eye.In comparison to the state-of-the-art Tiny YOLOv5,Tiny YOLOv6 and Tiny YOLOv8 models,the proposed EYE-YOLO achieved 6:23.32%higher mAP.Originality/value:This work addresses the problem of eye disease recognition as a bounding box regression and detection problem.Whereas,the work in the related research is largely based on eye disease classification.The other highlight of this work is to propose a richly annotated dataset for different eye diseases useful for training deep learning-based object detectors.The major highlight of this work lies in the proposal of an improved variant of the Tiny YOLOv7 model focusing on eye disease detection.The proposed modifications in the Tiny YOLOv7 aided the proposed model in achieving better results as compared to the state-of-the-art Tiny YOLOv8 and YOLOv8 Nano.展开更多
The Histograms of Oriented Gradients(HOG)can produce good results in an image target recognition mission,but it requires the same size of the target images for classification of inputs.In response to this shortcoming,...The Histograms of Oriented Gradients(HOG)can produce good results in an image target recognition mission,but it requires the same size of the target images for classification of inputs.In response to this shortcoming,this paper performs spatial pyramid segmentation on target images of any size,gets the pixel size of each image block dynamically,and further calculates and normalizes the gradient of the oriented feature of each block region in each image layer.The new feature is called the Histogram of Spatial Pyramid Oriented Gradients(HSPOG).This approach can obtain stable vectors for images of any size,and increase the target detection rate in the image recognition process significantly.Finally,the article verifies the algorithm using VOC2012 image data and compares the effect of HOG.展开更多
Audio-visual scene classification(AVSC)poses a formidable challenge owing to the intricate spatial-temporal relationships exhibited by audio-visual signals,coupled with the complex spatial patterns of objects and text...Audio-visual scene classification(AVSC)poses a formidable challenge owing to the intricate spatial-temporal relationships exhibited by audio-visual signals,coupled with the complex spatial patterns of objects and textures found in visual images.The focus of recent studies has predominantly revolved around extracting features from diverse neural network structures,inadvertently neglecting the acquisition of semantically meaningful regions and crucial components within audio-visual data.The authors present a feature pyramid attention network(FPANet)for audio-visual scene understanding,which extracts semantically significant characteristics from audio-visual data.The authors’approach builds multi-scale hierarchical features of sound spectrograms and visual images using a feature pyramid representation and localises the semantically relevant regions with a feature pyramid attention module(FPAM).A dimension alignment(DA)strategy is employed to align feature maps from multiple layers,a pyramid spatial attention(PSA)to spatially locate essential regions,and a pyramid channel attention(PCA)to pinpoint significant temporal frames.Experiments on visual scene classification(VSC),audio scene classification(ASC),and AVSC tasks demonstrate that FPANet achieves performance on par with state-of-the-art(SOTA)approaches,with a 95.9 F1-score on the ADVANCE dataset and a relative improvement of 28.8%.Visualisation results show that FPANet can prioritise semantically meaningful areas in audio-visual signals.展开更多
Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially...Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially leading to false positives or missed detections.To solve these problems,the YOLOv8 network is enhanced by adding deformable convolution and atrous spatial pyramid pooling(ASPP),along with the integration of a coordinate attention(CA)mechanism.This allows the network to focus on small targets while expanding the receptive field without losing resolution.At the same time,context information on the target is gathered and feature expression is enhanced by attention modules in different directions.It effectively improves the positioning accuracy and achieves good results on the LUNA16 dataset.Compared with other detection algorithms,it improves the accuracy of pulmonary nodule detection to a certain extent.展开更多
Aiming at the problems of low detection efficiency and difficult positioning of traditional steel surface defect detection methods,a lightweight steel surface defect detection model based on you only look once version...Aiming at the problems of low detection efficiency and difficult positioning of traditional steel surface defect detection methods,a lightweight steel surface defect detection model based on you only look once version 7(YOLOv7)is proposed.First,a cascading style sheets(CSS)block module is proposed,which uses more lightweight operations to obtain redundant information in the feature map,reduces the amount of computation,and effectively improves the detection speed.Secondly,the improved spatial pyramid pooling with cross stage partial convolutions(SPPCSPC)structure is adopted to ensure that the model can also pay attention to the defect location information while predicting the defect category information,obtain richer defect features.In addition,the convolution operation in the original model is simplified,which significantly reduces the size of the model and helps to improve the detection speed.Finally,using efficient intersection over union(EIOU)loss to focus on high-quality anchors,speed up convergence and improve positioning accuracy.Experiments were carried out on the Northeastern University-defect(NEU-DET)steel surface defect dataset.Compared with the original YOLOv7 model,the number of parameters of this model was reduced by 40%,the frames per second(FPS)reached 112,and the average accuracy reached 79.1%,the detection accuracy and speed have been improved,which can meet the needs of steel surface defect detection.展开更多
With the rise in drowning accidents in swimming pools,the demand for the precision and speed in artificial intelligence(AI)drowning detection methods has become increasingly crucial.Here,an improved YOLO-based method,...With the rise in drowning accidents in swimming pools,the demand for the precision and speed in artificial intelligence(AI)drowning detection methods has become increasingly crucial.Here,an improved YOLO-based method,named DrownACB-YOLO,for drowning detection in swimming pools is proposed.Since existing methods focus on the drowned state,a transition label is added to the original dataset to provide timely alerts.Following this expanded dataset,two improvements are implemented in the original YOLOv5.Firstly,the spatial pyramid pooling(SPP)module and the default upsampling operator are replaced by the atrous spatial pyramid pooling(ASPP)module and the content-aware reassembly of feature(CARAFE)module,respectively.Secondly,the cross stage partial bottleneck with three convolutions(C3)module at the end of the backbone is replaced with the bottleneck transformer(BotNet)module.The results of comparison experiments demonstrate that DrownACB-YOLO performs better than other models.展开更多
基金supported by the National Natural Science Foundation of China(71401052)the Fundamental Research Funds for the Central Universities(2019B19514)。
文摘In order to solve the problem that existing multivariate grey incidence models cannot be applied to time series on different scales, a new model is proposed based on spatial pyramid pooling.Firstly, local features of multivariate time series on different scales are pooled and aggregated by spatial pyramid pooling to construct n levels feature pooling matrices on the same scale. Secondly,Deng's multivariate grey incidence model is introduced to measure the degree of incidence between feature pooling matrices at each level. Thirdly, grey incidence degrees at each level are integrated into a global incidence degree. Finally, the performance of the proposed model is verified on two data sets compared with a variety of algorithms. The results illustrate that the proposed model is more effective and efficient than other similarity measure algorithms.
基金the National Key Research and Development Program of China(No.2017YFB1302900)the National Natural Science Foundation of China(Nos.81971709,M-0019,and 82011530141)+2 种基金the Foundation of Science and Technology Commission of Shanghai Municipality(Nos.19510712200,and 20490740700)the Shanghai Jiao Tong University Foundation on Medical and Technological Joint Science Research(Nos.ZH2018ZDA15,YG2019ZDA06,and ZH2018QNA23)the 2020 Key Research Project of Xiamen Municipal Government(No.3502Z20201030)。
文摘Sinus floor elevation with a lateral window approach requires bone graft(BG)to ensure sufficient bone mass,and it is necessary to measure and analyse the BG region for follow-up of postoperative patients.However,the BG region from cone-beam computed tomography(CBCT)images is connected to the margin of the maxillary sinus,and its boundary is blurred.Common segmentation methods are usually performed manually by experienced doctors,and are complicated by challenges such as low efficiency and low precision.In this study,an auto-segmentation approach was applied to the BG region within the maxillary sinus based on an atrous spatial pyramid convolution(ASPC)network.The ASPC module was adopted using residual connections to compose multiple atrous convolutions,which could extract more features on multiple scales.Subsequently,a segmentation network of the BG region with multiple ASPC modules was established,which effectively improved the segmentation performance.Although the training data were insufficient,our networks still achieved good auto-segmentation results,with a dice coefficient(Dice)of 87.13%,an Intersection over Union(Iou)of 78.01%,and a sensitivity of 95.02%.Compared with other methods,our method achieved a better segmentation effect,and effectively reduced the misjudgement of segmentation.Our method can thus be used to implement automatic segmentation of the BG region and improve doctors’work efficiency,which is of great importance for developing preliminary studies on the measurement of postoperative BG within the maxillary sinus.
基金This project was funded by the Deanship of Scientific Research(DSR)at King Abdul Aziz University,Jeddah,under Grant No.KEP-10-611-42.The authors,therefore,acknowledge with thanks DSR technical and financial support.
文摘Face recognition is a big challenge in the research field with a lot of problems like misalignment,illumination changes,pose variations,occlusion,and expressions.Providing a single solution to solve all these problems at a time is a challenging task.We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching.The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching andmax-pooling.Finally,the input image is recognized using a robust kernel representation method using extracted features.The qualitative and quantitative analysis of the proposed method is carried on benchmark image datasets.Experimental results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR,ORL,LFW,and FERET face recognition datasets.
文摘Purpose:The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images.Furthermore,this work presents an improved variant of the Tiny YOLOv7 model developed specifically for eye disease detection.The model proposed in this work is a highly useful tool for the development of applications for autonomous detection of eye diseases in fundus images that can help and assist ophthalmologists.Design/methodology/approach:The approach adopted to carry out this work is twofold.Firstly,a richly annotated dataset consisting of eye disease classes,namely,cataract,glaucoma,retinal disease and normal eye,was created.Secondly,an improved variant of the Tiny YOLOv7 model was developed and proposed as EYE-YOLO.The proposed EYE-YOLO model has been developed by integrating multi-spatial pyramid pooling in the feature extraction network and Focal-EIOU loss in the detection network of the Tiny YOLOv7 model.Moreover,at run time,the mosaic augmentation strategy has been utilized with the proposed model to achieve benchmark results.Further,evaluations have been carried out for performance metrics,namely,precision,recall,F1 Score,average precision(AP)and mean average precision(mAP).Findings:The proposed EYE-YOLO achieved 28%higher precision,18%higher recall,24%higher F1 Score and 30.81%higher mAP than the Tiny YOLOv7 model.Moreover,in terms of AP for each class of the employed dataset,it achieved 9.74%higher AP for cataract,27.73%higher AP for glaucoma,72.50%higher AP for retina disease and 13.26%higher AP for normal eye.In comparison to the state-of-the-art Tiny YOLOv5,Tiny YOLOv6 and Tiny YOLOv8 models,the proposed EYE-YOLO achieved 6:23.32%higher mAP.Originality/value:This work addresses the problem of eye disease recognition as a bounding box regression and detection problem.Whereas,the work in the related research is largely based on eye disease classification.The other highlight of this work is to propose a richly annotated dataset for different eye diseases useful for training deep learning-based object detectors.The major highlight of this work lies in the proposal of an improved variant of the Tiny YOLOv7 model focusing on eye disease detection.The proposed modifications in the Tiny YOLOv7 aided the proposed model in achieving better results as compared to the state-of-the-art Tiny YOLOv8 and YOLOv8 Nano.
基金partly supported by the National Natural Science Foundation of China(No.51802348)。
文摘The Histograms of Oriented Gradients(HOG)can produce good results in an image target recognition mission,but it requires the same size of the target images for classification of inputs.In response to this shortcoming,this paper performs spatial pyramid segmentation on target images of any size,gets the pixel size of each image block dynamically,and further calculates and normalizes the gradient of the oriented feature of each block region in each image layer.The new feature is called the Histogram of Spatial Pyramid Oriented Gradients(HSPOG).This approach can obtain stable vectors for images of any size,and increase the target detection rate in the image recognition process significantly.Finally,the article verifies the algorithm using VOC2012 image data and compares the effect of HOG.
基金Shenzhen Institute of Artificial Intelligence and Robotics for Society,Grant/Award Number:AC01202201003-02GuangDong Basic and Applied Basic Research Foundation,Grant/Award Number:2024A1515010252Longgang District Shenzhen's“Ten Action Plan”for Supporting Innovation Projects,Grant/Award Number:LGKCSDPT2024002。
文摘Audio-visual scene classification(AVSC)poses a formidable challenge owing to the intricate spatial-temporal relationships exhibited by audio-visual signals,coupled with the complex spatial patterns of objects and textures found in visual images.The focus of recent studies has predominantly revolved around extracting features from diverse neural network structures,inadvertently neglecting the acquisition of semantically meaningful regions and crucial components within audio-visual data.The authors present a feature pyramid attention network(FPANet)for audio-visual scene understanding,which extracts semantically significant characteristics from audio-visual data.The authors’approach builds multi-scale hierarchical features of sound spectrograms and visual images using a feature pyramid representation and localises the semantically relevant regions with a feature pyramid attention module(FPAM).A dimension alignment(DA)strategy is employed to align feature maps from multiple layers,a pyramid spatial attention(PSA)to spatially locate essential regions,and a pyramid channel attention(PCA)to pinpoint significant temporal frames.Experiments on visual scene classification(VSC),audio scene classification(ASC),and AVSC tasks demonstrate that FPANet achieves performance on par with state-of-the-art(SOTA)approaches,with a 95.9 F1-score on the ADVANCE dataset and a relative improvement of 28.8%.Visualisation results show that FPANet can prioritise semantically meaningful areas in audio-visual signals.
文摘Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially leading to false positives or missed detections.To solve these problems,the YOLOv8 network is enhanced by adding deformable convolution and atrous spatial pyramid pooling(ASPP),along with the integration of a coordinate attention(CA)mechanism.This allows the network to focus on small targets while expanding the receptive field without losing resolution.At the same time,context information on the target is gathered and feature expression is enhanced by attention modules in different directions.It effectively improves the positioning accuracy and achieves good results on the LUNA16 dataset.Compared with other detection algorithms,it improves the accuracy of pulmonary nodule detection to a certain extent.
基金supported by the National Natural Science Foundation of China(No.62103298)the Natural Science Foundation of Hebei Province(No.F2018209289)。
文摘Aiming at the problems of low detection efficiency and difficult positioning of traditional steel surface defect detection methods,a lightweight steel surface defect detection model based on you only look once version 7(YOLOv7)is proposed.First,a cascading style sheets(CSS)block module is proposed,which uses more lightweight operations to obtain redundant information in the feature map,reduces the amount of computation,and effectively improves the detection speed.Secondly,the improved spatial pyramid pooling with cross stage partial convolutions(SPPCSPC)structure is adopted to ensure that the model can also pay attention to the defect location information while predicting the defect category information,obtain richer defect features.In addition,the convolution operation in the original model is simplified,which significantly reduces the size of the model and helps to improve the detection speed.Finally,using efficient intersection over union(EIOU)loss to focus on high-quality anchors,speed up convergence and improve positioning accuracy.Experiments were carried out on the Northeastern University-defect(NEU-DET)steel surface defect dataset.Compared with the original YOLOv7 model,the number of parameters of this model was reduced by 40%,the frames per second(FPS)reached 112,and the average accuracy reached 79.1%,the detection accuracy and speed have been improved,which can meet the needs of steel surface defect detection.
文摘With the rise in drowning accidents in swimming pools,the demand for the precision and speed in artificial intelligence(AI)drowning detection methods has become increasingly crucial.Here,an improved YOLO-based method,named DrownACB-YOLO,for drowning detection in swimming pools is proposed.Since existing methods focus on the drowned state,a transition label is added to the original dataset to provide timely alerts.Following this expanded dataset,two improvements are implemented in the original YOLOv5.Firstly,the spatial pyramid pooling(SPP)module and the default upsampling operator are replaced by the atrous spatial pyramid pooling(ASPP)module and the content-aware reassembly of feature(CARAFE)module,respectively.Secondly,the cross stage partial bottleneck with three convolutions(C3)module at the end of the backbone is replaced with the bottleneck transformer(BotNet)module.The results of comparison experiments demonstrate that DrownACB-YOLO performs better than other models.
