Xiao Chongjing, Zhou Zhiyu, Hu Yanjun
School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China.
J Imaging. 2025 May 19;11(5):162. doi: 10.3390/jimaging11050162.
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.
水下目标图像处理是海洋环境探测的一项关键技术。海洋环境的复杂性通常导致水下目标图像出现颜色偏差、对比度失衡和模糊等问题。现有的水下目标语义分割方法要么分割精度低，要么无法满足水下硬件的轻量化要求。为应对这些挑战，本研究提出了一种基于DeepLabv3+的轻量级语义分割模型。该框架采用MobileOne-S0作为轻量级主干进行特征提取，将简单、无参数注意力模块（SimAM）集成到深层特征层中，用带状池化替换空洞空间金字塔池化（ASPP）模块中的全局平均池化，并采用基于内容引导注意力（CGA）的混合融合方案，在最小化参数冗余的同时有效结合高级和低级特征。实验结果表明，该模型在DUT-USEG数据集上的平均交并比（mIoU）达到71.18%，参数和计算复杂度分别降至6.628M和39.612G FLOPs。这些进展在保持模型效率的同时显著提高了分割精度，使该模型非常适合资源受限的水下应用。
