The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safetyand performing routine tunnel maintenance. The automatic and accurate detection of cracks on the su...The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safetyand performing routine tunnel maintenance. The automatic and accurate detection of cracks on the surface of roadtunnels is the key to improving the maintenance efficiency of road tunnels. Machine vision technology combinedwith a deep neural network model is an effective means to realize the localization and identification of crackdefects on the surface of road tunnels.We propose a complete set of automatic inspection methods for identifyingcracks on the walls of road tunnels as a solution to the problem of difficulty in identifying cracks during manualmaintenance. First, a set of equipment applied to the real-time acquisition of high-definition images of walls inroad tunnels is designed. Images of walls in road tunnels are acquired based on the designed equipment, whereimages containing crack defects are manually identified and selected. Subsequently, the training and validationsets used to construct the crack inspection model are obtained based on the acquired images, whereas the regionscontaining cracks and the pixels of the cracks are finely labeled. After that, a crack area sensing module is designedbased on the proposed you only look once version 7 model combined with coordinate attention mechanism (CAYOLOV7) network to locate the crack regions in the road tunnel surface images. Only subimages containingcracks are acquired and sent to the multiscale semantic segmentation module for extraction of the pixels to whichthe cracks belong based on the DeepLab V3+ network. The precision and recall of the crack region localizationon the surface of a road tunnel based on our proposed method are 82.4% and 93.8%, respectively. Moreover, themean intersection over union (MIoU) and pixel accuracy (PA) values for achieving pixel-level detection accuracyare 76.84% and 78.29%, respectively. The experimental results on the dataset show that our proposed two-stagedetection method outperforms other state-of-the-art models in crack region localization and detection. Based onour proposedmethod, the images captured on the surface of a road tunnel can complete crack detection at a speed often frames/second, and the detection accuracy can reach 0.25 mm, which meets the requirements for maintenanceof an actual project. The designed CA-YOLO V7 network enables precise localization of the area to which a crackbelongs in images acquired under different environmental and lighting conditions in road tunnels. The improvedDeepLab V3+ network based on lightweighting is able to extract crack morphology in a given region more quicklywhile maintaining segmentation accuracy. The established model combines defect localization and segmentationmodels for the first time, realizing pixel-level defect localization and extraction on the surface of road tunnelsin complex environments, and is capable of determining the actual size of cracks based on the physical coordinatesystemafter camera calibration. The trainedmodelhas highaccuracy andcanbe extendedandapplied to embeddedcomputing devices for the assessment and repair of damaged areas in different types of road tunnels.展开更多
To proceed from sensation to movement,integration and transformation of information from different senses and reference frames are required.Several brain areas are involved in this transformation process,but previous ...To proceed from sensation to movement,integration and transformation of information from different senses and reference frames are required.Several brain areas are involved in this transformation process,but previous neuroanatomical and neurophysiological studies have implicated the caudal area 7b as one particular component of this transformation system.In this study,we present the first quantitative report on the spatial coding properties of caudal area 7b.The results showed that neurons in this area had intermediate component characteristics in the transformation system;the area contained bimodal neurons,and neurons in this area encode spatial information using a hybrid reference frame.These results provide evidence that caudal area 7b may belong to the reference frame transformation system,thus contributing to our general understanding of the transformation system.展开更多
To proceed from sensation to movement, integration and transformation of information from different senses and reference frames are required. Several brain areas are involved in this transformation process, but previo...To proceed from sensation to movement, integration and transformation of information from different senses and reference frames are required. Several brain areas are involved in this transformation process, but previous neuroanatomical and neurophysiological studies have implicated the caudal area 7b as one particular component of this transformation system. In this study, we present the first quantitative report on the spatial coding properties of caudal area 7b. The results showed that neurons in this area had intermediate component characteristics in the transformation system; the area contained bimodal neurons, and neurons in this area encode spatial information using a hybrid reference frame. These results provide evidence that caudal area 7b may belong to the reference frame transformation system, thus contributing to our general understanding of the transformation system.展开更多
基金the Changsha Science and Technology Plan 2004081in part by the Science and Technology Program of Hunan Provincial Department of Transportation 202117in part by the Science and Technology Research and Development Program Project of the China Railway Group Limited 2021-Special-08.
文摘The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safetyand performing routine tunnel maintenance. The automatic and accurate detection of cracks on the surface of roadtunnels is the key to improving the maintenance efficiency of road tunnels. Machine vision technology combinedwith a deep neural network model is an effective means to realize the localization and identification of crackdefects on the surface of road tunnels.We propose a complete set of automatic inspection methods for identifyingcracks on the walls of road tunnels as a solution to the problem of difficulty in identifying cracks during manualmaintenance. First, a set of equipment applied to the real-time acquisition of high-definition images of walls inroad tunnels is designed. Images of walls in road tunnels are acquired based on the designed equipment, whereimages containing crack defects are manually identified and selected. Subsequently, the training and validationsets used to construct the crack inspection model are obtained based on the acquired images, whereas the regionscontaining cracks and the pixels of the cracks are finely labeled. After that, a crack area sensing module is designedbased on the proposed you only look once version 7 model combined with coordinate attention mechanism (CAYOLOV7) network to locate the crack regions in the road tunnel surface images. Only subimages containingcracks are acquired and sent to the multiscale semantic segmentation module for extraction of the pixels to whichthe cracks belong based on the DeepLab V3+ network. The precision and recall of the crack region localizationon the surface of a road tunnel based on our proposed method are 82.4% and 93.8%, respectively. Moreover, themean intersection over union (MIoU) and pixel accuracy (PA) values for achieving pixel-level detection accuracyare 76.84% and 78.29%, respectively. The experimental results on the dataset show that our proposed two-stagedetection method outperforms other state-of-the-art models in crack region localization and detection. Based onour proposedmethod, the images captured on the surface of a road tunnel can complete crack detection at a speed often frames/second, and the detection accuracy can reach 0.25 mm, which meets the requirements for maintenanceof an actual project. The designed CA-YOLO V7 network enables precise localization of the area to which a crackbelongs in images acquired under different environmental and lighting conditions in road tunnels. The improvedDeepLab V3+ network based on lightweighting is able to extract crack morphology in a given region more quicklywhile maintaining segmentation accuracy. The established model combines defect localization and segmentationmodels for the first time, realizing pixel-level defect localization and extraction on the surface of road tunnelsin complex environments, and is capable of determining the actual size of cracks based on the physical coordinatesystemafter camera calibration. The trainedmodelhas highaccuracy andcanbe extendedandapplied to embeddedcomputing devices for the assessment and repair of damaged areas in different types of road tunnels.
基金This study was funded by the National Science Foundation of China(NSFC 30770700,30670669,30870825,30530270,31070963,and 31070965)the 973 program(2007CB947703 and 2011CB707800)+3 种基金the Key Program of the Chinese Academy of Sciences,China(KSCX2-EW-J-23,KSCX2-YW-R-261,and KSCX2-EW-R-11)the West Light Foundation of the Chinese Academy of Sciences(0902351081)the National Natural Science Foundation of China(30921064)the project sponsored by Yunnan Development and Reform Commission(2009-1988).
文摘To proceed from sensation to movement,integration and transformation of information from different senses and reference frames are required.Several brain areas are involved in this transformation process,but previous neuroanatomical and neurophysiological studies have implicated the caudal area 7b as one particular component of this transformation system.In this study,we present the first quantitative report on the spatial coding properties of caudal area 7b.The results showed that neurons in this area had intermediate component characteristics in the transformation system;the area contained bimodal neurons,and neurons in this area encode spatial information using a hybrid reference frame.These results provide evidence that caudal area 7b may belong to the reference frame transformation system,thus contributing to our general understanding of the transformation system.
基金funded by the National Science Foundation of China (NSFC 30770700, 30670669, 30870825,30530270, 31070963, and 31070965)the 973 program(2007CB947703 and 2011CB707800)+3 种基金the Key Program of the Chinese Academy of Sciences, China (KSCX2-EW-J-23, KSCX2-YW-R-261, and KSCX2-EW-R-11)the West Light Foundation of the Chinese Academy of Sciences (0902351081)the National Natural Science Foundation of China (30921064)the project sponsored by Yunnan Development and Reform Commission (2009-1988)
文摘To proceed from sensation to movement, integration and transformation of information from different senses and reference frames are required. Several brain areas are involved in this transformation process, but previous neuroanatomical and neurophysiological studies have implicated the caudal area 7b as one particular component of this transformation system. In this study, we present the first quantitative report on the spatial coding properties of caudal area 7b. The results showed that neurons in this area had intermediate component characteristics in the transformation system; the area contained bimodal neurons, and neurons in this area encode spatial information using a hybrid reference frame. These results provide evidence that caudal area 7b may belong to the reference frame transformation system, thus contributing to our general understanding of the transformation system.
