Segmentation and Abundance Estimation of Deep-Sea Polymetallic Nodule Images Based on AttMSD-Uformer

With advances in seafloor sensor data acquisition and deep-learning–based image interpretation, modern deep-sea resource exploration has progressed substantially; however, traditional semantic segmentation models remain challenged on seafloor optical mineral imagery due to pronounced multi-scale feature heterogeneity, object coalescence, and frequent missed detections of small-scale mineral targets. To this end, this study presents AttMSD-Uformer, a deep-learning semantic segmentation architecture that couples a dual-branch encoder with an attention-guided multi-scale decoder, and further introduces a semi-automated annotation pipeline that combines algorithmic recognition with expert-guided refinement to curate high-quality training datasets efficiently. Experimental results show that the proposed model delivers consistently strong segmentation performance across multiple seafloor mineral datasets and demonstrates robust cross-dataset generalization. Furthermore, this study systematically compared the predictive performance of four representative regression models using segmentation-derived image-level nodule coverage as the explanatory variable. The results indicate that the random-forest regressor is particularly well suited to the heterogeneous characteristics of seafloor polymetallic nodule imagery, excelling at modeling nonlinear and piecewise relationships. This methodology provides an efficient and reliable reference framework for intelligent quantitative assessment of seafloor resources. The code is available at https://github.com/jiahaoZhang-cugb/AttMSD-Uformer.