ECA-DeepLabV3+ Network-Based Segmentation of Deep-Sea Mussel Community

Deep-sea mussel communities typically congregate in regions with abundant combustible hydrate resources. Accurately identifying and segmenting the surface features and shapes of these communities is essential for automated detection technologies. Given the high heterogeneity of the marine environment and its impact on object boundary segmentation, this paper enhances channel-level features in the DeeplabV3+ backbone network to achieve accurate segmentation of sea mussel shapes and surface features. In this study, we collected thousands of deep-sea mussel image datasets and annotated and trained the datasets. To ensure the accuracy of mussel segmentation and recognition, the image is firstly pre-processed with edge sharpening and low illumination enhancement, then channel attention mechanisms-Squeeze and Excitation, Convolutional Block Attention Module, Efficient Channel Attention, and Coordinate Attention are integrated in the Bottleneck module and the Atrous Spatial Pyramid Pooling (ASPP) module of the backbone network. Experimental results show that integrating the ECA module in the Bottleneck module significantly improved the feature extraction capabilities of the network and increased the segmentation accuracy of the DeeplabV3+ model for deep-sea sea mussel images, improving the efficiency of combustible ice resource exploration.