Year: 2025
Type: article
Abstract: Accurate fish population estimation is crucial for fisheries management, ecological monitoring, and aquaculture optimization. Traditional manual counting methods are labor-intensive and error-prone, while existing automated approaches struggle with occlusions, small-object detection, and identity switches. To address these challenges, this paper proposes an improved fish counting framework integrating YOLOv8-DT for detection and Byte-OCSORT for tracking. YOLOv8-DT incorporates the Deformable Large Kernel Attention Cross Stage Partial (DLKA CSP) module for adaptive receptive field adjustment and the Triple Detail Feature Infusion (TDFI) module for enhanced multi-scale feature fusion, improving small-object detection and occlusion robustness. Byte-OCSORT extends OC-SORT by integrating ByteTrack’s two-stage matching and a Class-Aware Cost Matrix (CCM), reducing ID switches and improving multi-species tracking stability. Experimental results on real-world underwater datasets demonstrate that YOLOv8-DT achieves a mAP50 of 0.971 and mAP50:95 of 0.742, while Byte-OCSORT reaches a MOTA of 72.3 and IDF1 of 69.4, significantly outperforming existing methods, confirming the effectiveness of the proposed framework for robust and accurate fish counting in complex aquatic environments.
DOI: 10.3390/jmse13061016
Authors: Yan Li, Zhenpeng Wu, Ying Yu, Chichi Liu
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
SDG: Life below water
Open Access status: gold
Estimated APC paid: $3,137
Awards: G4134237583, G655911834
OpenAlex ID: W4410692783