Intelligent detection of fine cracks on sleepers based on improved YOLOv5 model of cascade fusion

The detection of cracks in sleepers is important in ensuring safe operation of trains and the crack width is a key index in this process. The traditional YOLOv5 model can not achieve satisfactory detection results in the face of large changes in the scale of the target and interference of background information in the detection process. In this paper, a method to detect and measure the fine crack of sleeper based on improved YOLOv5 model of cascade fusion and edge fitting algorithm is proposed to solve these problems. An adaptive multi-scale feature fusion technique is proposed to achieve multi-scale feature fusion efficiently and solve the problem of large changes in the scale of the target. An improved method of feature recombination of attention mechanism is used to solve the interference problem of background information. On this basis, a method based on cascade fusion is proposed to fuse the above improvements and reconstruct the structure of the YOLOv5 model. Moreover, a fitting algorithm of the crack edge is proposed to realize the precise measurement of the maximum width of the fine crack on sleepers. Finally, the process of crack detection and width measurement of sleeper is developed and the performance of the method proposed in this paper is verified by experiments through self-made data sets. The experimental results show that the precision of crack detection is increased from 84.08% to 96.86%, and the recall is increased from 78.13% to 96.46%. The increases are 12.78% and 18.33% respectively. At the same time, the average detection time is reduced from 15.23 ms to 10.04 ms. The measurement accuracy of the maximum crack width is 0.05 mm.