Work

Uncertainty Label-Driven Automated Tuna Catch Counting with Robust Object Tracking for Occlusion Fishing Vessels

Year: 2025

Type: article

Abstract: Accurate and automated catch counting is crucial for modern fishery management, particularly in pelagic longline tuna fisheries where traditional manual methods are inefficient and error-prone. While computer vision offers solutions, its performance in complex, occlusion-heavy deck environments remain challenging due to severe occlusion and motion blur. This study proposes an uncertainty label-driven method for automated tuna counting to address these issues. Our approach introduces a closed polygonal counting region for improved coverage, an “uncertainty” label to handle classification ambiguity, and an enhanced ByteTrack algorithm for consistent target tracking. A lightweight YOLOv8-based model, optimized with efficient modules, ensures real-time operation. Experimental results demonstrate that the improved model achieves a [email protected] of 0.958 and [email protected]:0.95 of 0.829, outperforming the baseline while reducing parameters and latency by 18.9% and 25.0%, respectively. In practical deck scenarios, the system reached 92.78% counting accuracy, a 14.43 percentage-point improvement. These findings validate the method’s enhanced robustness and stability under challenging conditions, offering a viable solution for automated catch monitoring and supporting further fishery informatization.

Source: Fishes

DOI: 10.3390/fishes10110557

Authors: Wenjie Xu, Hui Fang, Yang Dai, Wei Fan, Haoyu Wang, Haodong Yang

Institutions: Ministry of Agriculture and Rural Affairs, Shanghai Ocean University, Chinese Academy of Fishery Sciences

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Life below water

Open Access status: gold

Estimated APC paid: $3,137