To address the challenges of low detection efficiency and limited accuracy in identifying contamination on offshore photovoltaic platforms, this study proposes an enhanced YOLOv8-based algorithm for detecting salt deposit on photovoltaic modules. The SimAM parameter-free attention mechanism is integrated at the end of the backbone network and within the neck layers to improve feature representation of salt deposits under complex environmental conditions, thereby enhancing detection accuracy. In addition, the WIoU loss function is employed in place of the original CIoU loss to alleviate harmful gradients caused by low-quality data and to strengthen the generalization capability of the model. A dedicated dataset of salt accumulation images from offshore photovoltaic panels is constructed to support this targeted detection task. Experimental results demonstrate that the proposed algorithm achieves an mAP50 of 85.8%, a 3% improvement over YOLOv8, while maintaining a detection speed of 67 frames per second. These findings confirm that the proposed approach meets both the accuracy and efficiency requirements for automated detection of salt deposition on offshore photovoltaic modules.

As terrestrial resources become increasingly scarce, the exploration and utilization of marine resources have become crucial for ensuring a stable resource supply. A maritime A-Frame is a specialized lifting mechanism mounted on the stern of a vessel, designed for deploying and retrieving heavy loads during subsea exploration. Real-time monitoring of the stress of A-Frames is essential for identifying potential failures and preventing accidents. This paper presents a stress-monitoring campaign conducted on a maritime A-Frame during a deep-sea mining project in the South China Sea. Fiber Bragg Grating (FBG) strain sensors were installed on the A-Frame to measure its stress responses throughout the deep-sea mining operation. The stress variations observed during the deployment and retrieval of a deep-sea mining vehicle were analyzed. The results indicate that the stress caused by the swinging motion of the A-Frame was significantly higher than that generated by the lifting and deployment of the mining equipment. Additionally, a finite element model (FEM) of the A-Frame was developed to estimate the stress of the hot spots by integrating the measured strain data. The analysis confirmed that the maximum stress experienced by the A-Frame was well below the allowable threshold, indicating that the structure had sufficient strength to withstand operational loads. In addition, the swing angle of the A-Frame significantly affects the stress value of the A-Frame, while lifting the mining vehicle has a very slight effect. Thus, it is advisable to accelerate the deployment and retrieval speeds of the mining vehicle and minimize the outward swing angle of the A-Frame. These findings provide valuable insights for optimizing the design and ensuring the safe operation of maritime A-Frames in deep-sea mining exploration.