Real-Time Instance Segmentation of Deep-Sea Polymetallic Nodules with Wavelet Statistical Cross-Attention and Reverse Convolution Upsampling

Deep-sea polymetallic nodules are vital strategic mineral resources. However, residual low-contrast features from complex seabed conditions, densely overlapped targets and severe seabed clutter greatly degrade the accuracy and real-time performance of automatic segmentation. This paper proposes a lightweight instance segmentation model, PWC-YOLO. Firstly, a partial convolution-based C3k2-PConv module is constructed to reduce computational cost. Secondly, Wavelet Statistical Cross-Attention (WSCA) replaces the built-in self-attention of C2PSA to suppress underwater noise and enhance the feature expression of tiny nodules. Thirdly, Converse2D reverse convolution upsampling recovers lost contour details of nodules. A manually annotated dataset of Clarion–Clipperton Zone nodules is established for evaluation. Ablation experiments validate that the PWC-YOLO model reaches 95.2% BBox mAP50 and 95.8% Mask mAP50, with only 2.47 M parameters and 9.3 GFLOPs at an inference speed of 88 FPS. Comparative experiments indicate that PWC-YOLO achieves competitive mask segmentation accuracy with fewer parameters and lower computational overhead, yielding a favorable accuracy–complexity trade-off against mainstream segmentation models. Visual results verify favorable robustness within the tested deep-sea scenes, showing favorable lightweight properties with only 2.47 M parameters and 9.3 GFLOPs, which provides potential prerequisites for future lightweight deployment on underwater edge computing devices.