Real-time sow behavior detection based on deep learning

Recording sow behaviors allows tracking their health status, the timely detection of abnormalities, and provides assistance to increase their health both physically and mentally. In recent years, detecting sow behavior using machine vision technology has become a popular research topic. However, current detection methods are based on the premise that an individual pig can be accurately identified. In this paper, A Real-Time Sow Behavior Detection Algorithm based on Deep Learning (SBDA-DL) is proposed. The algorithm was used for real-time detection of three typical sow behaviors: drinking, urination, and mounting. The experimental results show that the average precision (AP) of the algorithm to detect drinking, urination, and mounting behaviors is 96.5%, 91.4%, and 92.3%, respectively. The mean average precision (mAP) of a category is 93.4%, which can reach 7 frames per second in commonly configured microcomputers. The algorithm uses an optimized deep learning network structure to directly detect the sow behavior. This improves the accuracy of behavior detection at a processing speed required for real-time detection and meets the requirements of daily monitoring from auxiliary staff in most pig breeding farms.