Cracks,potholes,and other defects often occur on infrastructure such as bridges,among which cracks are one of the most frequent defects.They have diverse shapes and are difficult to detect.Traditional manual inspectio...Cracks,potholes,and other defects often occur on infrastructure such as bridges,among which cracks are one of the most frequent defects.They have diverse shapes and are difficult to detect.Traditional manual inspection methods are inefficient and have low accuracy,while automated inspection machines are bulky and inconvenient to carry and use.Based on the shortcomings of existing detection technologies,this paper proposes a portable structural surface crack detection system based on the Android platform using a portable hand-held image acquisition device.The system captures cracks on the structure's surface and obtains high-definition crack images.Then,these images are transmitted to portable smartphone terminals through Wi-Fi.Next,the image is pre-processed using weighted averaging,grayscale linear transformation,and adaptive median filtering.Then,the improved Canny edge detection algorithm is applied to identify crack information,and the edge segmentation algorithm is used to determine the crack width.Finally,based on camera calibration,the pixels are converted into the length data required for actual measurement.The results show that the system is easy to operate,and it not only has crack storage and tracking functions,but also can effectively measure the crack width on the surface of components.The measurement accuracy of this system reaches the sub-pixel level,and in actual testing,compared with the crack width gauge,the maximum relative error does notexceed6.25%.展开更多
With the rapid development of intelligent traffic information monitoring technology,accurate identification of vehicles,pedestrians and other objects on the road has become particularly important.Therefore,in order to...With the rapid development of intelligent traffic information monitoring technology,accurate identification of vehicles,pedestrians and other objects on the road has become particularly important.Therefore,in order to improve the recognition and classification accuracy of image objects in complex traffic scenes,this paper proposes a segmentation method of semantic redefine segmentation using image boundary region.First,we use the Seg Net semantic segmentation model to obtain the rough classification features of the vehicle road object,then use the simple linear iterative clustering(SLIC)algorithm to obtain the over segmented area of the image,which can determine the classification of each pixel in each super pixel area,and then optimize the target segmentation of the boundary and small areas in the vehicle road image.Finally,the edge recovery ability of condition random field(CRF)is used to refine the image boundary.The experimental results show that compared with FCN-8s and Seg Net,the pixel accuracy of the proposed algorithm in this paper improves by 2.33%and 0.57%,respectively.And compared with Unet,the algorithm in this paper performs better when dealing with multi-target segmentation.展开更多
基金Supported by Shaanxi Provincial Key Research and Development Program(2024GX-YBXM-288)the National Natural Science Foundation of China(52172324)+1 种基金Beilin District Science and Technology Program(GX2350)the Special Fund Project for Basic Research Business Expenses of Central level Public Welfare Research Institutes(2023-9062)。
文摘Cracks,potholes,and other defects often occur on infrastructure such as bridges,among which cracks are one of the most frequent defects.They have diverse shapes and are difficult to detect.Traditional manual inspection methods are inefficient and have low accuracy,while automated inspection machines are bulky and inconvenient to carry and use.Based on the shortcomings of existing detection technologies,this paper proposes a portable structural surface crack detection system based on the Android platform using a portable hand-held image acquisition device.The system captures cracks on the structure's surface and obtains high-definition crack images.Then,these images are transmitted to portable smartphone terminals through Wi-Fi.Next,the image is pre-processed using weighted averaging,grayscale linear transformation,and adaptive median filtering.Then,the improved Canny edge detection algorithm is applied to identify crack information,and the edge segmentation algorithm is used to determine the crack width.Finally,based on camera calibration,the pixels are converted into the length data required for actual measurement.The results show that the system is easy to operate,and it not only has crack storage and tracking functions,but also can effectively measure the crack width on the surface of components.The measurement accuracy of this system reaches the sub-pixel level,and in actual testing,compared with the crack width gauge,the maximum relative error does notexceed6.25%.
基金Supported in part by the Shaanxi Natural Science Basic Research Program(2022JM-298)the National Natural Science Foundation of China(52172324)+1 种基金Shaanxi Provincial Key Research and Development Program(2021SF-483)the Science and Technology Project of Shaan Provincal Transportation Department(21-202K,20-38T)。
文摘With the rapid development of intelligent traffic information monitoring technology,accurate identification of vehicles,pedestrians and other objects on the road has become particularly important.Therefore,in order to improve the recognition and classification accuracy of image objects in complex traffic scenes,this paper proposes a segmentation method of semantic redefine segmentation using image boundary region.First,we use the Seg Net semantic segmentation model to obtain the rough classification features of the vehicle road object,then use the simple linear iterative clustering(SLIC)algorithm to obtain the over segmented area of the image,which can determine the classification of each pixel in each super pixel area,and then optimize the target segmentation of the boundary and small areas in the vehicle road image.Finally,the edge recovery ability of condition random field(CRF)is used to refine the image boundary.The experimental results show that compared with FCN-8s and Seg Net,the pixel accuracy of the proposed algorithm in this paper improves by 2.33%and 0.57%,respectively.And compared with Unet,the algorithm in this paper performs better when dealing with multi-target segmentation.
