Rapid acquisition and surface defects recognition based on panoramic image of small-section hydraulic tunnel

• Rapid image acquisition equipment for small-section hydraulic tunnels with different cross-section forms and diameters. • Improved YOLOv5 object detection model incorporating DenseNet and ECA modules. • A “point-ring-section” detection method based on panoramic images. • Accurate identification and positioning of the boundary of large-area defects. Small-section hydraulic tunnels are characterized by small spaces and various section forms, under complex environments, which makes it difficult to carry out an inspection by the mobile acquisition equipment. To resolve these problems, an arbitrarily adjustable camera module deployment method and the corresponding automatic image acquisition equipment with multi-area array cameras are proposed and developed. Such method enables the acquisition of full-length surface images of the hydraulic tunnels with different cross-section forms and diameters by a one-way travel, and the overlap rate and accuracy of the acquired image sets meet the requirements of three-dimensional reconstruction and panoramic image generation. In addition, to improve the speed and accuracy of traditional algorithms for tunnel surface defects detection, this paper proposes an improved YOLOv5s-DECA model. The algorithm introduces DenseNet to optimize the backbone feature extraction network and incorporates an efficient channel attention ECA module to make a better extraction of features of defects. The experimental results show that mAP, and F1-score of YOLOv5-DECA are 73.4% and 74.6%, respectively, which are better than the common model in terms of accuracy and robustness. The proposed YOLOv5-DECA has great detection performance for targets with variable shapes and can solve the problem of classification imbalance in surface defects. Then, by combining YOLOv5-DECA with the direction search algorithm, a “point-ring-section” method is established to allow rapid identification of common surface defects by detecting them layer by layer with the bottom image of the stitched panorama as the seed. The presented method in this paper effectively solves the problem that a single image fails to show the overall distribution of the defects and their accurate positioning in a whole large tunnel section and the effective features of defects in an excessively large panoramic image size are difficult to be captured by the neural network. Field applications demonstrated that the presented method is adequate for high-precision and intelligent surface defect detection and positioning for different small-section hydraulic tunnels such as circular, arch-wall, and box-shaped hydraulic tunnels.