The concrete aging problem has gained more attention in recent years as more bridges and tunnels in the United States lack proper maintenance. Though the Federal Highway Administration requires these public concrete s...The concrete aging problem has gained more attention in recent years as more bridges and tunnels in the United States lack proper maintenance. Though the Federal Highway Administration requires these public concrete structures to be inspected regularly, on-site manual inspection by human operators is time-consuming and labor-intensive. Conventional inspection approaches for concrete inspection, using RGB imagebased thresholding methods, are not able to determine metric information as well as accurate location information for assessed defects for conditions. To address this challenge, we propose a deep neural network(DNN) based concrete inspection system using a quadrotor flying robot(referred to as City Flyer) mounted with an RGB-D camera. The inspection system introduces several novel modules. Firstly, a visual-inertial fusion approach is introduced to perform camera and robot positioning and structure 3 D metric reconstruction. The reconstructed map is used to retrieve the location and metric information of the defects.Secondly, we introduce a DNN model, namely Ada Net, to detect concrete spalling and cracking, with the capability of maintaining robustness under various distances between the camera and concrete surface. In order to train the model, we craft a new dataset, i.e., the concrete structure spalling and cracking(CSSC)dataset, which is released publicly to the research community.Finally, we introduce a 3 D semantic mapping method using the annotated framework to reconstruct the concrete structure for visualization. We performed comparative studies and demonstrated that our Ada Net can achieve 8.41% higher detection accuracy than Res Nets and VGGs. Moreover, we conducted five field tests, of which three are manual hand-held tests and two are drone-based field tests. These results indicate that our system is capable of performing metric field inspection,and can serve as an effective tool for civil engineers.展开更多
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
In light of the needs of China’s foreign economic and trade development and the regulations of the Law of Commodity Inspection, the State Administration of Import and Export Commodity Inspection issued a readjusted C...In light of the needs of China’s foreign economic and trade development and the regulations of the Law of Commodity Inspection, the State Administration of Import and Export Commodity Inspection issued a readjusted Category List of Import and Export Commodities Subject to Inspection Implemented by the Institutions of Commodity Inspection, and it is implemented officially on July 1, 1995. The new category list is compiled on the classified basis of the Commodity Names and Code Coordination System used widely around the world. The commodity code and name are identical to the Commodity List of the Custom Statisticsrelating to which there are 2。展开更多
The China Society for Human Rights Studies （CSHRS） organized on Nov. 28 a forum to study and implement the guidelines of the third plenary session of the 18th Central Committee of the Communist Party of China （CPC...The China Society for Human Rights Studies （CSHRS） organized on Nov. 28 a forum to study and implement the guidelines of the third plenary session of the 18th Central Committee of the Communist Party of China （CPC）. Executive members of CSHRS and other CSHRS members in Beijing discussed improvement of judicial protection for human rights and legal construction in China. CSHRS President Luo Haocai, who was vice chairman of the 10th National Committee of the Chinese People＇s Political Consultative Conference, delivered a speech at the forum.展开更多
基金supported in part by the U.S.National Science Foundation(IIP-1915721)the U.S.Department of TransportationOffice of the Assistant Secretary for Research and Technology(USDOTOST-R)(69A3551747126)through INSPIRE University Transportation Center(http//inspire-utc.mst.edu)at Missouri University of Science and Technology。
文摘The concrete aging problem has gained more attention in recent years as more bridges and tunnels in the United States lack proper maintenance. Though the Federal Highway Administration requires these public concrete structures to be inspected regularly, on-site manual inspection by human operators is time-consuming and labor-intensive. Conventional inspection approaches for concrete inspection, using RGB imagebased thresholding methods, are not able to determine metric information as well as accurate location information for assessed defects for conditions. To address this challenge, we propose a deep neural network(DNN) based concrete inspection system using a quadrotor flying robot(referred to as City Flyer) mounted with an RGB-D camera. The inspection system introduces several novel modules. Firstly, a visual-inertial fusion approach is introduced to perform camera and robot positioning and structure 3 D metric reconstruction. The reconstructed map is used to retrieve the location and metric information of the defects.Secondly, we introduce a DNN model, namely Ada Net, to detect concrete spalling and cracking, with the capability of maintaining robustness under various distances between the camera and concrete surface. In order to train the model, we craft a new dataset, i.e., the concrete structure spalling and cracking(CSSC)dataset, which is released publicly to the research community.Finally, we introduce a 3 D semantic mapping method using the annotated framework to reconstruct the concrete structure for visualization. We performed comparative studies and demonstrated that our Ada Net can achieve 8.41% higher detection accuracy than Res Nets and VGGs. Moreover, we conducted five field tests, of which three are manual hand-held tests and two are drone-based field tests. These results indicate that our system is capable of performing metric field inspection,and can serve as an effective tool for civil engineers.
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.
文摘In light of the needs of China’s foreign economic and trade development and the regulations of the Law of Commodity Inspection, the State Administration of Import and Export Commodity Inspection issued a readjusted Category List of Import and Export Commodities Subject to Inspection Implemented by the Institutions of Commodity Inspection, and it is implemented officially on July 1, 1995. The new category list is compiled on the classified basis of the Commodity Names and Code Coordination System used widely around the world. The commodity code and name are identical to the Commodity List of the Custom Statisticsrelating to which there are 2。
文摘The China Society for Human Rights Studies （CSHRS） organized on Nov. 28 a forum to study and implement the guidelines of the third plenary session of the 18th Central Committee of the Communist Party of China （CPC）. Executive members of CSHRS and other CSHRS members in Beijing discussed improvement of judicial protection for human rights and legal construction in China. CSHRS President Luo Haocai, who was vice chairman of the 10th National Committee of the Chinese People＇s Political Consultative Conference, delivered a speech at the forum.
