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

Frontiers in Marine Science, Journal Year: 2025, Volume and Issue: 12

Published: April 1, 2025

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

Language: Английский

Efficient tuna detection and counting with improved YOLOv8 and ByteTrack in pelagic fisheries

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103116 - 103116

Published: April 1, 2025

Language: Английский