Abstract:
Objectives Under the coupling action of environment and loads, dam underwater structures suffer from defects, affecting the safety, stability, and functional performance of the project. Underwater robots like remotely operated vehicles (ROV) equipped with visible light cameras can realize the high-resolution spatial information in a non-contact form for underwater damage. However, it is still a challenging task that needs to be solved urgently to efficiently extract effective information from massive image and video data.
Methods First, we propose a real-time multi-class defect automatic identification framework for dam underwater structures. Specifically, the single-stage object detection network YOLOv5-s is utilized as the base model to develop the damage detector. Then, the model sparsity and pruning strategies are combined to change the batch layer weight distribution and remove model redundant parameters. Next, transfer learning and knowledge distillation are combined to recover the accuracy degradation caused by model pruning and compression.
Results As an example, the underwater detection of a high dam is considered. The effectiveness of the proposed method is validated in complex underwater scenes like obstacle occlusion, low visibility, and uneven illumination.
Conclusions The experimental results indicate that the proposed method can effectively overcome the interference of complicated underwater imaging environments and accurately identify different types of defects. Moreover, the proposed method achieves an inference speed of processing 100 defect images per second, demonstrating its real-time detection capability.
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