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

doi:10.1117/12.2652058

20 January 2023 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

Proceedings Volume 12561, AOPC 2022: Atmospheric and Environmental Optics; 125610D (2023) https://doi.org/10.1117/12.2652058
Event: Applied Optics and Photonics China 2022 (AOPC2022), 2022, Beijing, China

Abstract

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.

Citation Download Citation

Jiongjiang Chen, Jialin Tang, Zhuang Zhou, Binghua Su, WanXin Liang, Yunting Lai, Dujuan Zhou, and Chenhao Ma "Underwater image segmentation based on multi-scale residual attention", Proc. SPIE 12561, AOPC 2022: Atmospheric and Environmental Optics, 125610D (20 January 2023); https://doi.org/10.1117/12.2652058

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