Underwater wireless power transfer (UWPT) technology can improve the endurance of unmanned underwater vehicles (UUVs). The stability and efficiency of UWPT depend on the success rate of UUV docking. A novel detection model, TFDF-YOLO, is proposed for dynamic position identification of UUV docking. First, a spatial–frequency decoupling (SFD) module is proposed by using Fourier-based degradation cues to guide Top-K proxy attention to boost blurred edge extraction capability. A relevance-difference fusion (RD-Fusion) strategy is improved by a global channel attention mechanism to realize multi-scale feature recognition. Furthermore, a new adaptive loss function (U-CIoU) is developed to suppress illumination bias and anchor inflation. Results on a reliable multi-source dataset demonstrate that the proposed model achieves 91.5% accuracy and 92.7% mAP@0.5. This work could enhance the success rate and reliability of UWPT. It shows potential for broader underwater applications, including deep-sea docking and multi-AUV cooperative systems.
Yin et al. (Thu,) studied this question.