A Lightweight Semantic Segmentation Model for Underwater Images Based on DeepLabv3+

Underwater object image processing is a crucial technology for marine environmental exploration. The complexity of marine environments typically results in underwater object images exhibiting color deviation, imbalanced contrast, and blurring. Existing semantic segmentation methods for underwater objects either suffer from low segmentation accuracy or fail to meet the lightweight requirements of underwater hardware. To address these challenges, this study proposes a lightweight semantic segmentation model based on DeepLabv3+. The framework employs MobileOne-S0 as the lightweight backbone for feature extraction, integrates Simple, Parameter-Free Attention Module (SimAM) into deep feature layers, replaces global average pooling in the Atrous Spatial Pyramid Pooling (ASPP) module with strip pooling, and adopts a content-guided attention (CGA)-based mixup fusion scheme to effectively combine high-level and low-level features while minimizing parameter redundancy. Experimental results demonstrate that the proposed model achieves a mean Intersection over Union (mIoU) of 71.18% on the DUT-USEG dataset, with parameters and computational complexity reduced to 6.628 M and 39.612 G FLOPs, respectively. These advancements significantly enhance segmentation accuracy while maintaining model efficiency, making the model highly suitable for resource-constrained underwater applications.