Abstract
China is gradually transitioning from the “tunnel construction era” to the “tunnel maintenance era,” and more and more operating tunnels need to be inspected for diseases. With the continuous development of computer vision, the automatic identification of tunnel lining cracks with computers has gradually been applied in engineering. On the basis of summarizing the weaknesses and strengths of previous studies, this paper first uses the improved multiscale Retinex algorithm to filter the collected tunnel crack images and introduces the eight-direction Sobel edge detection operator to extract the edges of the cracks. Perform mathematical morphological operations on the image after edge extraction, and use the principle of the smallest enclosing rectangle to remove the isolated points of the image. Finally, the performance of the algorithm is judged by the objective evaluation index of the image, the accuracy of crack recognition, and the running time of the algorithm. The image filtering algorithm proposed in this paper can better preserve the edges of the image while enhancing the image. The objective evaluation indexes of the image have been improved significantly, and the main body of the crack can be accurately identified. The overall crack recognition accuracy rate can reach 97.5%, which is higher than the existing tunnel lining crack recognition algorithm, and the average calculation time for each image is shorter. This algorithm has high research significance and engineering application value.
Highlights
Tunnels are an important part of the road and railway transportation network
Based on the abovementioned problems, this paper proposes a new tunnel lining crack identification algorithm based on improved multiscale Retinex and Sobel edge detection
Compare the cracks that can be recognized by the human eye in the original image with the cracks that the computer automatically recognizes. e part of the crack contour drawn manually in the original image is shown in red in Figure 9, all the correct pixels are marked as Pt, and the other redundant parts in Figure 9 are represented in green in the original image, marked as Pf. e total number of pixels is denoted by N
Summary
Tunnels are an important part of the road and railway transportation network. With the continuous increase in the mileage of roads and railways in China, and due to the aging of the structure itself, a large number of tunnels have gradually entered the maintenance period. e tunnel itself is faced with its own structural aging and damage from external factors. En, according to the obtained tunnel lining image, a detection method based on computer vision is used to identify the cracks in the image. In order to better identify and analyze cracks, Shi et al [5] proposed an analysis method based on crack width characteristics, which greatly reduces the interference of “false cracks” and improves the accuracy of crack identification. Existing various edge detection algorithms are not satisfactory for image recognition results, and the time and accuracy of crack extraction need to be further improved. The existing image preprocessing methods are basically perfect, there are still environments with poor tunnel lighting, and image recognition needs to be enhanced. Based on the abovementioned problems, this paper proposes a new tunnel lining crack identification algorithm based on improved multiscale Retinex and Sobel edge detection
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