Underwater image segmentation based on multi-scale residual attention
Jiongjiang Chen, Jialin Tang, Zhuang Zhou, Binghua Su, Wanxin Liang, Yunting Lai, Dujuan Zhou, Chenhao Ma · 2023
In this paper, we propose a multi-scale residual attention network (MSR-Net) segmentation algorithm, which uses the ResNet50 residual network as the backbone feature extraction network and introduces a multi-scale channel attention mechanism. The MSR-Net uses the ResNet50 residual network as the backbone feature extraction network and introduces a multi-scale channel attention mechanism, which enables the network model to retain more complete sample edge information, significantly improves the segmentation capability of the model and ensures its network performance, which can effectively meet the needs of underwater image segmentation-related tasks. The proposed network is tested on the DUT-USEG dataset, and the recall, accuracy and average cross-merge ratio are 74.17%, 83.21% and 65.96%, respectively. As shown by the experimental results, compared with the classical U-Net, PSPNet and DeepLabV3, the performance indexes of the method in this paper are significantly improved.