## 1 Introduction  
Tunas are an integral part of marine ecosystems, playing a crucial role in maintaining their stability. Furthermore, they account for nearly 20% of the value of marine capture fisheries, contributing significantly to global food security and economic stability [1]. However, recent surveys indicate that tuna populations are declining significantly, primarily due to overfishing [2; 3; 4]. To achieve sustainable tuna fishing, preventing overfishing is essential, and two critical factors in this effort are the accurate identification of tuna species and the precise counting of their quantities. The accuracy of species identification is directly linked to the effectiveness of refined fisheries management. Through refined management, fisheries' authorities can establish scientific and reasonable fishing quotas based on the species and quantities of tuna caught, thus ensuring appropriate protection for different tuna species. Certain species, such as bluefin tuna, face more severe existential threats, making the accurate identification of their proportion in the catch vital for setting and adjusting quotas. If these species cannot be reliably identified, it becomes difficult to implement effective management measures, thus increasing the risk of overfishing. Currently, the identification of tuna aboard fishing vessels relies predominantly on manual labor. While experienced sailors can accurately identify most tuna species under ideal conditions, such as when not constrained by visual angles, their accuracy significantly diminishes in real-world fishing environments, especially during high-intensity operations. Factors like visual fatigue, adverse weather conditions, and complex backgrounds can lead to identification errors. These errors directly affect the classification of tuna species, resulting in distorted fishing data and an inaccurate basis for setting quotas. To mitigate these issues, an intelligent real-time system for species recognition on fishing vessels is needed. This system must be capable of automatically identifying the species and quantities of tuna caught, providing essential data for effective fishery management. An improved ability to recognize tuna not only increases transparency in the fishing process but also enables fisheries' management to adjust strategies based on real-time data, thus preventing overfishing. Most importantly, accurate identification directly influences the possibility of real-time monitoring and dynamic quota adjustments. When the catch of a particular species approaches or exceeds its quota, the intelligent system can issue warnings based on the identification results, thus alerting fishers to cease further fishing activities. This dynamic management mechanism hinges on high-precision recognition algorithms; any errors in the identification process could compromise the entire management chain. Thus, building an intelligent real-time recognition system capable of operating in the complex environment of fishing vessels is crucial. This system must handle noisy and complex backgrounds, partial blurring, and color distortion while maintaining high recognition accuracy. By ensuring reliable species identification, the system can provide robust data support for refined fishery management, thus significantly enhancing the scientific rigor and practicality of fishing regulations. Ultimately, this will contribute to the goal of sustainable tuna fishing. This paper introduces the RSNC-YOLO algorithm for recognizing tuna in complex scenes, which incorporates several key improvements based on YOLOv8 [5]. 1. RepC3 [6] Block Replacement: Some C2f blocks were replaced with RepC3 blocks to accelerate the inference speed of the model while maintaining high accuracy. 2. SCDown [7] Block Introduction: Certain Conv blocks were substituted with SCDown blocks to reduce the model's complexity without sacrificing accuracy. 3.