Tuna accounts for 20% of the output value of global marine capture fisheries, and it plays a crucial role in maintaining ecosystem stability, ensuring global food security, and supporting economic stability. However, improper management has led to significant overfishing, resulting in a sharp decline in tuna populations. For sustainable tuna fishing, it is essential to accurately identify the species of tuna caught and to count their numbers, as these data are the foundation for setting scientific catch quotas. The traditional manual identification method suffers from several limitations and is prone to errors during prolonged operations, especially due to factors like fatigue, high-intensity workloads, or adverse weather conditions, which ultimately compromise its accuracy. Furthermore, the lack of transparency in the manual process may lead to intentional underreporting, which undermines the integrity of fisheries’ data. In contrast, an intelligent, real-time identification system can reduce the need for human labor, assist in more accurate identification, and enhance transparency in fisheries’ management. This system not only provides reliable data for refined management but also enables fisheries’ authorities to dynamically adjust fishing strategies in real time, issue timely warnings when catch limits are approached or exceeded, and prevent overfishing, thus ultimately contributing to sustainable tuna management. In light of this need, this article proposes the RSNC-YOLO algorithm, an intelligent model designed for recognizing tuna in complex scenarios on fishing vessels. Based on YOLOv8s-seg, RSNC-YOLO integrates Reparameterized C3 (RepC3), Selective Channel Down-sampling (SCDown), a Normalization-based Attention Module (NAM), and C2f-DCNv3-DLKA modules. By utilizing a subset of images selected from the Fishnet Open Image Database, the model achieves a 2.7% improvement in mAP@0.5 and a 0.7% improvement in mAP@0.5:0.95. Additionally, the number of parameters is reduced by approximately 30%, and the model’s weight size is reduced by 9.6 MB, while maintaining an inference speed comparable to that of YOLOv8s-seg.

To reduce costs associated with the detection and population assessment of Antarctic krill and diversify the single detection approach, our team designed and deployed a drifting sonar buoy for krill detection in the waters surrounding Antarctica. The experimental results indicate that the drifting sonar buoy fulfills its primary functions and meets the requirements for krill detection in designated marine areas. The initial experiment lasted seven days, during which the buoy collected 157 records of speed and location data as well as 82 records of sea surface temperature and acoustic data, demonstrating its potential for krill detection. The experiment also revealed shortcomings in the initial design of the drifting sonar buoy, leading to proposed improvements. The paper further compares the advantages and disadvantages of the drifting sonar buoy and traditional fishing vessels in krill detection with the buoy offering unique benefits in low-cost deployment, labor savings, broad monitoring range, and continuous real-time data monitoring. The drifting sonar buoy serves as an excellent complement to fishing vessels in krill detection.

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 mAP@0.5 of 0.958 and mAP@0.5:0.95 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.