## 4 Results and Discussion
### Algorithm Performance Evaluation  
As illustrated in Figure 6, the model's mask loss on the validation set begins to overfit after approximately 150 epochs, while the box loss continues to decline steadily. Given the nature of the recognition task, where the primary purpose of instance segmentation is to provide more fine-grained features for the model rather than to segment the tuna instances themselves, this overfitting does not negatively impact the model's performance. Overall, the trends in loss for the improved YOLOv8 show a steady decrease, and the metrics for Precision, Recall, and Average Precision consistently increase, indicating the strong adaptability and generalization ability of the improved model for this task. In Figure 6c,d, a clear comparison between the results of RSNC-YOLO and YOLOv8s-seg demonstrates the superior performance of RSNC-YOLO. Specifically, the curve representing RSNC-YOLO rises more steadily compared to YOLOv8s-seg, indicating a more consistent improvement in accuracy and model performance across different thresholds. This steady rise suggests that RSNC-YOLO maintains a more balanced Precision-Recall trade-off, which is crucial in object detection tasks where both accuracy and reliability in varying conditions are needed. Furthermore, the stability of the RSNC-YOLO curve implies that the model is less prone to drastic fluctuations in performance, making it more robust in complex and challenging environments, such as those involving noisy or partially obscured images. This improvement underscores the effectiveness of the enhancements made to the YOLOv8 architecture, particularly in handling real-world scenarios where Precision and Recall need to be optimized concurrently. The comparison of RSNC-YOLO and the original model in Figure 7 showed that while the Precision-Recall (P-R) curves for tuna recognition followed similar trends, the overall results of RSNC-YOLO were slightly better. Notably, there was a significant differ  
Figure 6: Results of the RSNC-YOLO. (**a**) Loss curves for the RSNC-YOLO training process. (**b**) Loss curves for the RSNC-YOLO validation process. (**c**) Comparison of [EMAIL_ADDRESS] of YOLOv8-seg and RSNC-YOLO. (**d**) Comparison of [EMAIL_ADDRESS] of YOLOv8-seg and RSNC-YOLO. ence in the P-R curves for the \"others\" category between RSNC-YOLO and the original model. This discrepancy suggested that the original YOLOv8 occasionally misidentified other fish caught on the fishing boat as tuna. Given that the Fishnet open-access image database contains distinct features for different fish species, such misidentifications were unacceptable for fine-grained feature recognition tasks. In contrast, RSNC-YOLO showed almost no misidentification of other fish as tuna, indicating that it had stronger feature representation capabilities and was better suited for recognizing tuna in complex scenes with fine-grained accuracy. The closer the curve is to the upper right corner, the better the model's performance. In the case of the improved model, the [EMAIL_ADDRESS] for the \"others\" category is very high,  
Figure 7: Precision–Recall curves of RSNC-YOLO and YOLOv8. (**a**) The curve represents the Precision–Recall curve of RSNC-YOLO. (**b**) The curve shows the Precision–Recall curve of the original YOLOv8-seg. which results in the Precision-Recall curve for the \"others\" category being almost aligned with the top and right edges of the graph. As a result, the \"others\" Precision-Recall curve is barely visible in Figure 7a. However, in Figure 7b, the \"others\" curve is not as close to the top and right edges, indicating that YOLOv8 tends to misclassify non-tuna species as tuna. This misclassification is detrimental to the accurate count and management of tuna species. The results presented in Figure 8 illustrate the model's performance before and after improvements in the fine-grained task of recognizing tuna. Comparing the recognition outcomes between the improved model and YOLOv8-seg reveals significant enhancements in some complex scenes.