Accurate estimation of tuna catch is crucial for effective pelagic fishery management and resource conservation.However, existing manual counting methods suffer from issues such as Twin Metal HDBD/FTBD/Rails low accuracy and poor timeliness, highlighting the urgent need for an efficient and automated solution.This paper proposes an automatic tuna counting method based on the YOLOv8n-DMTNet target detection algorithm combined with the improved ByteTrack tracking algorithm.The method uses YOLOv8n as the base model, enhanced with detail-enhanced convolution and a multi-scale feature fusion pyramid network, which significantly improves detection accuracy in complex marine environments.
Additionally, a dynamic, task-aligned detection head is introduced to optimize the synergy between classification and localization tasks.To further improve counting accuracy, the ByteTrack algorithm is employed for target tracking, and a region-specific counting method is designed to prevent double counting and omission due to occlusion and motion irregularities.Experimental results show that the improved YOLOv8n-DMTNet model achieves a 9.2% increase in mAP@0.
5 and a 6.4% increase in [email protected]:0.95 compared to YOLOv8n in the tuna detection task, while reducing the number of parameters by 42.
3% and computational complexity by 33.3%.The counting accuracy reaches 93.5%, and the method demonstrates superior performance in terms of accuracy, robustness, and computational resource efficiency, making it well-suited for resource-constrained fishing vessel environments.
This approach provides reliable technical support shoes for automated catch counting in pelagic fisheries.