Abstract: Challenges posed by hydraulic tunnels, such as confined space, varying cross-sections, complex environments, and lack of signal, hinder rapid image acquisition. To address these challenges, a rapid image acquisition system that can capture cross-sectional images of the tunnel while traveling in a short period is presented. This system can capture images under different cross-sectional shapes and sizes and meets the acquisition overlap rate and accuracy requirements of three-dimensional reconstruction and image generator for lining expansion. To enhance the speed and accuracy of the traditional algorithms, the YOLOv5 detection algorithm is improved using a DenseNet network and an efficient channel attention module, thereby enhancing the effectiveness of feature extraction. In addition, a "segment-ring-point" method is proposed for the efficient identification of common surface defects on tunnel linings using panoramic images, with the bottom image of the panorama serving as the seed point. This approach addresses the limitations of single images that fail to reflect the overall distribution of the damage and large images that do not capture the effective features of the network. Field applications demonstrate the following: (1) The improved YOLOv5 model significantly improves the mean average precision and F₁ performances compared with YOLOv7-tiny, YOLOv8, and YOLOv9 models, effectively improving the accuracy of defect identification and boundary positioning in complex environments and substantially reducing the occurrence of false positives and missed detections. (2) This approach is suitable for rapid acquisition and panoramic image stitching in hydraulic tunnels with diverse cross-sectional shapes and sizes. It can also be used for identifying and locating surface diseases promptly in tunnels to provide defect treatment measures.

Key words: hydraulic tunnels, image acquisition, panorama image, rapid detection, accurate localization, YOLOv5, DenseNet