Leveraging YOLO for Real-Time Video Analysis of Animal Welfare in Pig Slaughtering Processes

Christian Beecks, Anandraj Amalraj, Alexander Graß, Marc Jentsch, Felix Kitschke, Maximilian Norz, Patric Schäffer

Abstract: Artificial intelligence has empowered digitalization into a new era of intelligent systems. Machine learning solutions are being tailored to various application scenarios, leading to automated functionalities along complex real-world processes. In this paper, we investigate the domain of animal welfare and present our latest findings in relation to the automated detection of animal welfare violations. To this end, we introduce three different situations of increased animal welfare risk occurring in a pig slaughtering process and elucidate YOLO-based approaches to detect these situations based on video data. Though the reported results are considered to be preliminary, our solution already detects most of the situations of increased animal welfare risk with high accuracy.