Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented performance.However,existing CNN-based SISR network structure design consider mostly only cha...Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented performance.However,existing CNN-based SISR network structure design consider mostly only channel or spatial information,and cannot make full use of both channel and spatial information to improve SISR performance further.The present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial information.Specifically,we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a very deep network.This structure allows each dense residual group to apply a local residual skip connection and enables the cascading of multiple residual blocks to reuse previous features.A mixed attention module is inserted into each dense residual group,to enable the algorithm to fuse channel attention with laplacian spatial attention effectively,and thereby more adaptively focus on valuable feature learning.The qualitative and quantitative results of extensive experiments have demonstrate that the proposed method has a comparable performance with other stateof-the-art 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
In the process of hard rock tunnel excavation,workers often need to enter the tunnel boring machine(TBM)cutterhead at regular intervals to measure cutter wear.However,this method is time-consuming and labor-intensive....In the process of hard rock tunnel excavation,workers often need to enter the tunnel boring machine(TBM)cutterhead at regular intervals to measure cutter wear.However,this method is time-consuming and labor-intensive.Existing cutter prediction models primarily rely on geological parameters to predict overall cutter wear before construction,making it challenging to monitor real-time wear at different locations of the cutter and obtain accurate geological parameters.To address these challenges,this paper proposes a multi-head mixed attention mechanism-based method for real-time wear prediction of TBM disc cutter.First,a method of cutter wear normalization to eliminate measurement noise is explored.Then,considering the complex correlation of TBM operating parameters in feature and time dimensions,a new multi-head mixed attention mechanism model is designed to establish the dependency between different features and different moments,to better establish the mapping model between operating parameters and cutter wear.Finally,the current cutter wear state can be calculated by accumulating the wear amount of all previous small excavation sections.The effectiveness of the method is verified by using field data from the Mumbai tunnel.The results demonstrate that the method is capable of real-time prediction of front cutter and edge cutter wear on the test set,achieving an average accuracy rate of 95.75%.Moreover,the method can update the cutter wear status after every meter of excavation,which has good real-time performance.In addition,the average accuracy of cutter wear prediction of the proposed method is 11.75%,10.375%,3.875%,3.625%,1.375%,3.125%,2.25%,and 0.75%higher than that of LSTM,CNN,LSTM-CNN,CACNN,SACNN,MMADNN,MTACNN,and MFACNN.In summary,this approach offers an accurate prediction of cutter wear state while reducing inspection time and costs and has high application value.展开更多
To overcome the obstacles of poor feature extraction and little prior information on the appearance of infrared dim small targets,we propose a multi-domain attention-guided pyramid network(MAGPNet).Specifically,we des...To overcome the obstacles of poor feature extraction and little prior information on the appearance of infrared dim small targets,we propose a multi-domain attention-guided pyramid network(MAGPNet).Specifically,we design three modules to ensure that salient features of small targets can be acquired and retained in the multi-scale feature maps.To improve the adaptability of the network for targets of different sizes,we design a kernel aggregation attention block with a receptive field attention branch and weight the feature maps under different perceptual fields with attention mechanism.Based on the research on human vision system,we further propose an adaptive local contrast measure module to enhance the local features of infrared small targets.With this parameterized component,we can implement the information aggregation of multi-scale contrast saliency maps.Finally,to fully utilize the information within spatial and channel domains in feature maps of different scales,we propose the mixed spatial-channel attention-guided fusion module to achieve high-quality fusion effects while ensuring that the small target features can be preserved at deep layers.Experiments on public datasets demonstrate that our MAGPNet can achieve a better performance over other state-of-the-art methods in terms of the intersection of union,Precision,Recall,and F-measure.In addition,we conduct detailed ablation studies to verify the effectiveness of each component in our network.展开更多
This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight...This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic efficiency.Firstly a regional multi-agent Q-learning framework is proposed,which can equivalently decompose the global Q value of the traffic system into the local values of several regions Based on the framework and the idea of human-machine cooperation,a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to realtime traffic flow densities.In order to achieve better cooperation inside each region,a lightweight spatio-temporal fusion feature extraction network is designed.The experiments in synthetic real-world and city-level scenarios show that the proposed RegionS TLight converges more quickly,is more stable,and obtains better asymptotic performance compared to state-of-theart models.展开更多
[Objective]Detecting dense and small aquaculture net cages in complex backgrounds is difficult,the purpose of this study is to build a specialized dataset and design a targeted detection model that enhances recognitio...[Objective]Detecting dense and small aquaculture net cages in complex backgrounds is difficult,the purpose of this study is to build a specialized dataset and design a targeted detection model that enhances recognition accuracy and robustness for practical aquaculture management.[Methods]A dataset of aquaculture net cages was constructed using highresolution remote sensing imagery collected from seven representative farming regions(Australia,Canada,Chile,Croatia,Greece,China,and the Faroe Islands),and Cage-YOLO,a deep learning model based on YOLOv5,was proposed for detecting dense and small aquaculture net cages.First,an adaptive dense perception algorithm was introduced,which automatically selects and generates feature maps that reflect the high-density distribution of small aquaculture net cages.Second,an enhanced module based on spatial pyramid pooling fast was integrated to effectively reduce background noise interference and improve global feature extraction capabilities.Finally,a mixed attention block was incorporated to further enhance the model's perception of dense and small objects.[Results and Discussions]Experimental results showed that the proposed Cage-YOLO achieved improvements over the original YOLOv5 in terms of precision,recall,and mean average precision by 5.6,21.8,and 17.4 percentage points,respectively.The model size was maintained at 16.9 MB,demonstrating both strong performance and deployment advantages.[Conclusions]This study provides a new approach for dense and small object detection and offers technical support for the intelligent management of marine cage aquaculture.展开更多
基金This work was supported in part by the Natural Science Foundation of China under Grant 62063004 and 61762033in part by the Hainan Provincial Natural Science Foundation of China under Grant 2019RC018 and 619QN246by the Postdoctoral Science Foundation under Grant 2020TQ0293.
文摘Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented performance.However,existing CNN-based SISR network structure design consider mostly only channel or spatial information,and cannot make full use of both channel and spatial information to improve SISR performance further.The present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial information.Specifically,we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a very deep network.This structure allows each dense residual group to apply a local residual skip connection and enables the cascading of multiple residual blocks to reuse previous features.A mixed attention module is inserted into each dense residual group,to enable the algorithm to fuse channel attention with laplacian spatial attention effectively,and thereby more adaptively focus on valuable feature learning.The qualitative and quantitative results of extensive experiments have demonstrate that the proposed method has a comparable performance with other stateof-the-art 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supported by the National Natural Science Foundation of China(Grant No.52375255)。
文摘In the process of hard rock tunnel excavation,workers often need to enter the tunnel boring machine(TBM)cutterhead at regular intervals to measure cutter wear.However,this method is time-consuming and labor-intensive.Existing cutter prediction models primarily rely on geological parameters to predict overall cutter wear before construction,making it challenging to monitor real-time wear at different locations of the cutter and obtain accurate geological parameters.To address these challenges,this paper proposes a multi-head mixed attention mechanism-based method for real-time wear prediction of TBM disc cutter.First,a method of cutter wear normalization to eliminate measurement noise is explored.Then,considering the complex correlation of TBM operating parameters in feature and time dimensions,a new multi-head mixed attention mechanism model is designed to establish the dependency between different features and different moments,to better establish the mapping model between operating parameters and cutter wear.Finally,the current cutter wear state can be calculated by accumulating the wear amount of all previous small excavation sections.The effectiveness of the method is verified by using field data from the Mumbai tunnel.The results demonstrate that the method is capable of real-time prediction of front cutter and edge cutter wear on the test set,achieving an average accuracy rate of 95.75%.Moreover,the method can update the cutter wear status after every meter of excavation,which has good real-time performance.In addition,the average accuracy of cutter wear prediction of the proposed method is 11.75%,10.375%,3.875%,3.625%,1.375%,3.125%,2.25%,and 0.75%higher than that of LSTM,CNN,LSTM-CNN,CACNN,SACNN,MMADNN,MTACNN,and MFACNN.In summary,this approach offers an accurate prediction of cutter wear state while reducing inspection time and costs and has high application value.
基金the Industry-University-Research Cooperation Fund Project of the Eighth Research Institute of China Aerospace Science and Technology Corporation(No.USCAST2021-5)。
文摘To overcome the obstacles of poor feature extraction and little prior information on the appearance of infrared dim small targets,we propose a multi-domain attention-guided pyramid network(MAGPNet).Specifically,we design three modules to ensure that salient features of small targets can be acquired and retained in the multi-scale feature maps.To improve the adaptability of the network for targets of different sizes,we design a kernel aggregation attention block with a receptive field attention branch and weight the feature maps under different perceptual fields with attention mechanism.Based on the research on human vision system,we further propose an adaptive local contrast measure module to enhance the local features of infrared small targets.With this parameterized component,we can implement the information aggregation of multi-scale contrast saliency maps.Finally,to fully utilize the information within spatial and channel domains in feature maps of different scales,we propose the mixed spatial-channel attention-guided fusion module to achieve high-quality fusion effects while ensuring that the small target features can be preserved at deep layers.Experiments on public datasets demonstrate that our MAGPNet can achieve a better performance over other state-of-the-art methods in terms of the intersection of union,Precision,Recall,and F-measure.In addition,we conduct detailed ablation studies to verify the effectiveness of each component in our network.
基金supported by the National Science and Technology Major Project(2021ZD0112702)the National Natural Science Foundation(NNSF)of China(62373100,62233003)the Natural Science Foundation of Jiangsu Province of China(BK20202006)。
文摘This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic efficiency.Firstly a regional multi-agent Q-learning framework is proposed,which can equivalently decompose the global Q value of the traffic system into the local values of several regions Based on the framework and the idea of human-machine cooperation,a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to realtime traffic flow densities.In order to achieve better cooperation inside each region,a lightweight spatio-temporal fusion feature extraction network is designed.The experiments in synthetic real-world and city-level scenarios show that the proposed RegionS TLight converges more quickly,is more stable,and obtains better asymptotic performance compared to state-of-theart models.
基金National Key Research and Development Program of China(2024YFD2400404)National Natural Science Foundation of China(62102243,42376194)Shanghai Sailing Program(21YF1417000)。
文摘[Objective]Detecting dense and small aquaculture net cages in complex backgrounds is difficult,the purpose of this study is to build a specialized dataset and design a targeted detection model that enhances recognition accuracy and robustness for practical aquaculture management.[Methods]A dataset of aquaculture net cages was constructed using highresolution remote sensing imagery collected from seven representative farming regions(Australia,Canada,Chile,Croatia,Greece,China,and the Faroe Islands),and Cage-YOLO,a deep learning model based on YOLOv5,was proposed for detecting dense and small aquaculture net cages.First,an adaptive dense perception algorithm was introduced,which automatically selects and generates feature maps that reflect the high-density distribution of small aquaculture net cages.Second,an enhanced module based on spatial pyramid pooling fast was integrated to effectively reduce background noise interference and improve global feature extraction capabilities.Finally,a mixed attention block was incorporated to further enhance the model's perception of dense and small objects.[Results and Discussions]Experimental results showed that the proposed Cage-YOLO achieved improvements over the original YOLOv5 in terms of precision,recall,and mean average precision by 5.6,21.8,and 17.4 percentage points,respectively.The model size was maintained at 16.9 MB,demonstrating both strong performance and deployment advantages.[Conclusions]This study provides a new approach for dense and small object detection and offers technical support for the intelligent management of marine cage aquaculture.
