Image segmentation and coverage estimation of deep-sea polymetallic nodules based on lightweight deep learning model

Deep-sea polymetallic nodules, abundant in critical metal elements, are a vital strategic mineral resource. Accordingly, the prompt, accurate, and high-speed acquisition of parameters and distribution data for these nodules is crucial for the effective exploration, evaluation, and identification of valuable deposits. Studies show that one of the primary parameters for assessing polymetallic nodules is the Coverage Rate. For real-time, accurate, and efficient computation of this parameter, this article proposes a streamlined segmentation model named YOLOv7-PMN. This model is particularly designed for analyzing seafloor video data. The model substitutes the YOLOv7 backbone with the lightweight feature extraction framework of MobileNetV3-Small and integrates multi-level Squeeze-and-Excitation attention mechanisms. These changes enhance detection accuracy, speed up inference, and reduce the model's overall size. The head network utilizes depth-wise separable convolution modules, significantly decreasing the number of model parameters. Compared to the original YOLOv7, the YOLOv7-PMN shows improved detection and segmentation performance for nodules of varying sizes. On the same dataset, the recall rate for nodules increases by 3% over the YOLOv7 model. Model parameters are cut by 61.78%, memory usage by the best weights is reduced by 61.15%, and inference speed for detection and segmentation rises to 65.79 FPS, surpassing the 25 FPS video capture rate. The model demonstrates strong generalization capabilities, lowering the requirements for video data quality and reducing dependency on extensive dataset annotations. In summary, YOLOv7-PMN is highly effective in processing seabed images of polymetallic nodules, which are characterized by varying target scales, complex environments, and diverse features. This model holds significant promise for practical application and broad adoption.