Subway Tunnel Crack Identification based on YOLOv5

In view of the complex environment in the tunnel and the uneven lighting of the acquisition system, the lining images produced shadows and low contrast, a method of automatic color equalization combined with Laplacian pyramid (LP-ACE algorithm for short) was proposed in this paper. The computational complexity is reduced from the original O(N^4) to O ), which significantly reduces the amount of image computation and greatly improves the working efficiency. Due to the problems such as short time to identify skylights for cracks in key areas of subway tunnel, slow efficiency of manual method, inaccurate and difficult identification, an improved algorithm for key areas of power plant based on YOLO v5 was proposed: SD-YOLO. Ghost module is used to replace the traditional convolutional module to reduce the model parameters and improve the detection accuracy. The feature learning and feature extraction of crack region images are enhanced by the fusion of CBAM focus mechanism modules, while the influence of background on detection results is weakened. The bidirectional feature pyramid network is used for multi-scale feature fusion to reduce redundant calculation and improve the ability of the algorithm to detect small targets. The SD-YOLO algorithm proposed in this paper performs well in real samples, with an average accuracy of 93.1%, 11.3 percentage points higher than the original model, and significantly reduced parameters compared with the original model. Compared with YOLOv5s under the condition of reducing parameters, the model reasoning speed and detection accuracy are significantly improved by the proposed method, which can be effectively applied to tunnel detection.