A Method of Full Section Surface High-speed Sensing for Track Safety Inspection

Inspection of rail transit safety includes many elements. Methods based on a single image cannot perceive the details of the entire track section quickly. Multi-image stitching is greatly affected by the turning and swinging of the inspection vehicle, and the exposure conditions of different cameras result in significant brightness deviations, which reduces the accuracy of the identification of diseases. A full section surface sensing method for track inspection based on multiple line array images is proposed to address the above issues. A checkerboard calibration board is used to establish a rigid connection relationship between each camera, and the horizontal and vertical resolutions are adjusted to be equal. An invariant feature of tracks is established based on the edges of track surface and bottom to calculate the deviation of straight, curved, and swinging sections. Improved YOLO v10 algorithm is used to automatically extract the above feature. An improved histogram matching method is used to restore the overall brightness and achieve uniform transition at the joint of images based on the statistical characteristics of the forward direction and constrained by the two sides of the tracks with little brightness difference. Data collection is conducted at a certain section of Wuhan Metro and Wuhan Railway Bureau, with inspection vehicle running at speeds of 10, 40, 60, 80, and 120 km/h. The restoration method for image offset based on improved YOLO v10 reaches an average error of 11.21 pixels, with a precision rate of 95.20%, a recall rate of 93.58%, and an average accuracy of 84.03%, in which the recall rate has an increasing of 0.51%, and a pass rate of 99.43 % for 80 km. The mean difference between the overall image and the constrained image after brightness adjustment is 0.682, and the standard deviation is only 0.344, which are improved by 61.38% and 83.38% respectively compared to existing methods. The experimental results show that the proposed surface detail perception method for track safety inspection can obtain high-resolution images of the entire track section with a position error of less than 4.5 mm and a grayscale error of less than 1.71 under high-speed condition, greatly improving the efficiency of track safety inspection.