Polymetallic nodules are spherical or ellipsoidal mineral aggregates formed naturally in deep-sea environments. They contain a variety of metallic elements and are important solid mineral resources on the seabed. How best to quickly and accurately identify polymetallic nodules is one of the key questions of marine development and deep-sea-mineral-resource utilization. We propose a method that uses YOLOv5s as a reference network and integrates the IoU (Intersection over Union) and the Wasserstein distance in the optimal transmission theory to accurately identify different sizes of polymetallic nodules. Experiment using deep-sea hyperspectral data obtained from the Peru Basin was performed. The results showed that better recognition effects were achieved when the fusion ratio of overlap and Wasserstein distance metric was 0.5, and the accuracy of the proposed algorithm reached 84.5%, which was 6.2% higher than that of the original baseline network. In addition, the rest of the performance indexes were also improved significantly compared to traditional methods.