Application of deep learning methods in behavior recognition of laying hens

Abstract

Poultry behaviors reflect the health status of poultry. For four behaviors of laying hens, such as standing, lying, feeding, and grooming, four deep learning methods for recognition were compared in this paper, as Efficientnet-YoloV3, YoloV4-Tiny, YoloV5, and Faster-RCNN. First, the behavior detection dataset was produced based on the monitoring video data. Then, four algorithms, Efficientnet-YoloV3, YoloV4-Tiny, YoloV5, and Faster-RCNN, were used for training respectively. Finally, using the validation set for recognition, we got the mAP values for the four algorithms: Efficientnet-YoloV3 had mAP values of 81.82% (standing), 88.36% (lying), 98.20% (feeding), 77.30% (grooming), and its FPS values were 9.83 in order; YoloV4-Tiny had mAP values of 65.50% (standing), 78.40% (lying), 94.51% (feeding), 62.70% (grooming), and their FPS values were 14.73 successively; YoloV5 had mAP values of 97.24% (standing), 98.61% (lying), 97.43% (feeding), 92.33% (grooming), and their FPS values were 55.55 successively; Faster-RCNN had mAP values were 95.40% (standing), 98.50% (lying), 99.10% (feeding), and 85.40% (grooming), and their FPS values were 3.54 respectively. The results showed that the YoloV5 algorithm was the optimal algorithm among the four algorithms and could meet the requirements for real-time recognition of laying hens’ behavior.