基于改进YOLO 11n的轻量化金鲳鱼摄食强度检测模型OA

In recirculating aquaculture systems,precise control of feeding amounts is essential for optimizing feed utilization and promoting the healthy growth of fish.Overfeeding can result in resource waste and water pollution,while underfeeding can diminish fish growth efficiency.To address the challenges posed by large parameter sizes and the difficulty of efficient deployment on edge devices inherent in traditional visual inspection models,focusing on Trachinotus ovatus,a lightweight detection method for assessing feeding intensity was proposed based on an improved YOLO 11n model.The proposed model replaced the convolutional layers in the traditional YOLO 11n with deformable convolution kernel modules(arbitrary kernel convolution,AKConv)and incorporated deformable neighborhood attention(deformable neighborhood attention,DN-Attention).This integration not only reduced the model's computational cost but also enhanced the detection accuracy of number of individuals ingested being fed.The number of individuals ingested and the corresponding time frames were input into a recurrent neural network(CNN-LSTM)model.Through unsupervised training,the feeding intensity of Trachinotus ovatus was classified into three levels:strong,weak,and none.Experimental results demonstrated that the improved YOLO 11n detection model achieved an accuracy of 87.6%,a recall of 81.5%,and an average precision of 88.1%.Compared with the traditional YOLO 11n model,the proposed model improved accuracy,recall,and average precision by 3.5,3.6,and 2.4 percentage points,respectively.This approach effectively balanced detection accuracy and model lightweightness,providing technical support for the edge deployment of intelligent feeding equipment.