Fish feeding behavior recognition via lightweight two stage network and satiety experiments.

With the advancement of industrial aquaculture, intelligent fish feeding has become pivotal in reducing feed and labor costs while enhancing fish welfare. Computer vision, as a non-invasive and efficient approach, has made significant strides in this domain. However, current research still faces three major issues: qualitative labels lead to models that produce only qualitative outputs; redundant information in images causes interference; and the high complexity of models hinders real-time application. To address these challenges, this study innovatively proposes the quantification of fish feeding behaviors through satiety experiments, enabling the generation of quantitative data labels. A two-stage recognition network is then designed to eliminate redundant information and enhance model performance. This network utilizes pose detection to extract key features, while a graph convolutional network (GCN) effectively models the topological relationships between fish posture and distribution, achieving a satiety classification accuracy of 98.1%. Furthermore, to reduce model complexity, lightweight RepSELAN and SPPSF modules were developed, resulting in a 31.4% reduction in parameters and a 26.2% decrease in computational load, with only a 0.11% decrease in mAP(B) and a 0.95% increase in mAP(P). Compared with existing methods, this approach outperforms conventional models in both accuracy and efficiency, providing a novel and efficient model foundation for developing intelligent feeding strategies.