Salt Deposit Detection on Offshore Photovoltaic Modules Using an Enhanced YOLOv8 Framework

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.