Target detection and cow standing behavior recognition based on YOLOv5 algorithm

Abstract

Accurate and effective behavior recognition of cows is the basis for realizing informationization, high efficiency and scale of animal husbandry farming. To address the limitations of traditional non-contact and contact for obtaining animal behavior information, this paper investigates the target detection based on YOLOv5 algorithm and the cow standing behavior recognition method for video analysis. This paper first introduces the target detection algorithm, then describes the target detection network model (YOLOv5Net), which extracts the relevant features of cow images and performs image target detection through training to recognize the standing behavior of cows in real time. To achieve effective recognition of cow standing and efficient extraction of cow targets in complex natural environments, the YOLOv5 model for cow standing recognition is explored[8]; finally, the implemented YOLOv5 model is evaluated and analyzed for environment modeling and target detection algorithm objectives, and the experimental results show that the experimental detection correctness accuracy is 97.6%, and the preprocessing time in detecting a single image is It can quickly and accurately identify the standing behavior of cows, which lays the foundation for basic behavior identification and localization of cows.