## 2 Related Work
Enhancement algorithms, adversarial generation of reconstructed images, parallel convolutional kernels, and instance segmentation techniques used in previous studies have addressed the challenge of fine-grained tuna recognition in complex scenes to some extent, but there remains room for improvement. For example, although enhancement algorithms can filter noise with minimal computational overhead, their effectiveness is limited. Image reconstruction methods can remove noise more effectively, but they often incur significantoverhead time and may introduce artifacts or distortions. Using various convolutional kernels enhances feature fusion capabilities, but it also increases the complexity of convolution operations, thereby reducing inference speed and storage efficiency. Similarly, instance segmentation provides more detailed feedback but comes with high computational costs. In contrast, the enhancements introduced in the RSNC-YOLO model help to overcome the challenges posed by these existing techniques. The RepC3 module employs a structural reparameterization technique that simplifies the convolutional structure during the inference process, thus dramatically improving computational efficiency. This enhancement addresses the issue of reduced inference speed in previous approaches due to increased convolutional complexity. By converting the model to a more computationally efficient form during deployment, RepC3 ensures a significant boost in inference speed without sacrificing accuracy. Additionally, the SCDown module, derived from YOLOv10 [7], greatly simplifies the downsampling process within the network, thus halving the computational cost of standard convolutional layers. This improvement directly addresses the high computational costs associated with parallel convolutional kernels and further enhances both inference speed and storage efficiency. The NAMAtention mechanism effectively suppresses irrelevant or insignificant features in complex environments, thereby concentrating on critical features related to tuna. This mechanism compensates for the limitations of previous enhancement algorithms in filtering noise within complex scenes and enhances the model's overall performance in noisy environments by focusing on key features. Furthermore, the C2f-DCNv3-DLKA module combines deformable convolution and large kernel convolution, thus significantly enhancing the model's ability to recognize tuna in various shapes and poses. Compared to the high computational costs of instance segmentation, this module maintains a low overhead while enabling detailed feature extraction. It not only increases the model's receptive field but also effectively reduces background interference, thereby improving accuracy in complex scenes. In summary, RSNC-YOLO achieves an optimal balance of accuracy, speed, and computational efficiency through the integration of these innovative modules. The synergy of reparameterization, attention mechanisms, and convolutional innovations renders the model more robust in handling complex scenes while maintaining an efficient inference speed and low memory requirements. This design makes RSNC-YOLO particularly suitable for real-time applications in tuna identification and sustainable fishery management.