Work

Underwater instance segmentation: a method based on channel spatial cross-cooperative attention mechanism and feature prior fusion

Year: 2025

Type: article

Abstract: In aquaculture, underwater instance segmentation methods offer precise individual identification and counting capabilities. However, due to the inherent unique optical characteristics and high noise in underwater imagery, existing underwater instance segmentation models struggle to accurately capture the global and local feature information of objects, leading to generally lower detection accuracy in underwater instance segmentation models. To address this issue, this study proposes a novel Channel Space Coordinates Attention (CSCA) attention module and a Channel A Prior Attention Fusion (CAPAF) feature fusion module, aiming to improve the accuracy of underwater instance segmentation. The CSCA module effectively captures local and global information by combining channel and spatial attention weight, while the CAPAF module optimizes feature fusion by removing redundant information through learnable parameters. Experimental results demonstrate significant improvements when these two modules are applied to the YOLOv8 model, with the [email protected] metric increasing by 3.2% and 2% on the UIIS underwater instance segmentation dataset. Furthermore, the instance segmentation accuracy is significantly improved on the UIIS and USIS10K datasets after these two modules are applied to other networks.

Source: Frontiers in Marine Science

DOI: 10.3389/fmars.2025.1557965

Authors: Zhiqian He, Lijie Cao, Xiaoqing Xu, Jianhao Xu

Institution: Dalian Ocean University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Clean water and sanitation, Life below water

Open Access status: gold

Estimated APC paid: $3,801