A robust real‐time method for identifying hydraulic tunnel structural defects using deep learning and computer vision

Abstract Robots with cameras provide a non‐contact information acquisition solution for hydraulic tunnels, while manual damage‐related information extraction is time‐consuming and costs labor. This study proposes a robust real‐time framework for identifying hydraulic tunnel underwater structural damage using deep learning and computer vision. First, a high‐performance detector is built via the You Only Look Once v5s and adaptively spatial feature fusion module. A series of comparative experiments are used to explore the setting of the sparsification and pruning ratios to trade off a balance between accuracy and efficiency. Model sparsity ratio of 0.01 with a pruning ratio of 0.3 can be combined to change the weight distribution in the batch normalization layer and reduce network redundant parameters for slimming. Then, model fine‐tuning with knowledge distillation is utilized to recover the accuracy degradation caused by pruning. A hydraulic tunnel is utilized as the case study, and three defects including rust, exfoliation, and calcification precipitate are utilized as research items. The performance of the models was evaluated based on detection accuracy, robustness, and efficiency. Five extreme attributes of underwater scenes, including oblique angles, high brightness, uneven illumination, low visibility, and obstacle interference, were considered to test model generalization and efficacy. Experimental results show it achieves good detection performance in complicated underwater scenes, achieving 0.814 precision, 0.980 recall, 0.889 F1_score, and 0.894 Mean Average Precision (mAP)@0.5 in the test set. Moreover, the proposed method achieved a 50 Frames Per Second (FPS) detection speed when detecting video with 1080p, indicating its real‐time detection capability.