A Tunnel Crack Detection Method Based on an Unmanned Aerial Vehicle (UAV) Equipped with a High-Speed Camera and Crack Recognition Algorithm Using Improved Multi-Scale Retinex and Prewitt–Otsu

In order to solve the problems of low efficiency and accuracy in the traditional detection of tunnel cracks, this paper proposes a tunnel crack detection method based on a UAV (unmanned aerial vehicle) equipped with a high-speed camera and a crack recognition algorithm using the improved multi-scale Retinex (MSR) algorithm and the Prewitt–Otsu algorithm, aiming to improve the accuracy and efficiency of detection. The tunnel crack detection method, based on a UAV equipped with a high-speed camera to acquire tunnel surface images, significantly improves the detection efficiency. The recognition method employs an improved multi-scale Retinex algorithm to process the acquired images, enhancing the details of the crack images and improving the contrast between cracks and the background. The enhanced images are input to the Prewitt–Otsu algorithm, which segments the crack image by combining Prewitt edge detection and Otsu thresholding. Finally, the pseudo-crack and isolated edges are removed by the minimum bounding rectangle principle. Using the UAV-collected tunnel surface images as targets, the tunnel crack recognition algorithm proposed in this paper is compared with other existing methods. The experimental results show that the method proposed in this paper improves the recognition ability of the small-texture features of the tunnel’s surface, and the overall crack recognition accuracy is higher than the existing methods. The proposed method not only enhances the efficiency of tunnel crack detection but also significantly improves the recognition accuracy, demonstrating substantial practical significance for tunnel maintenance and safety management.