Study on Poultry Pose Estimation Based on Multi-Parts Detection

SIMPLE SUMMARY: Poultry farming is an important part of China’s agriculture system. The automatic estimation of poultry posture can help to analyze the movement, behavior, and even health of poultry. In this study, a poultry pose-estimation system was designed, which realized the automatic pose estimation of a single broiler chicken using a multi-part detection method. The experimental results show that this method can obtain better pose-estimation results for a single broiler chicken with respect to precision, recall, and F1 score. The pose-estimation system designed in this study provides a new means to provide help for poultry pose/behavior researchers in the future. ABSTRACT: Poultry pose estimation is a prerequisite for evaluating abnormal behavior and disease prediction in poultry. Accurate pose-estimation enables poultry producers to better manage their poultry. Because chickens are group-fed, how to achieve automatic poultry pose recognition has become a problematic point for accurate monitoring in large-scale farms. To this end, based on computer vision technology, this paper uses a deep neural network (DNN) technique to estimate the posture of a single broiler chicken. This method compared the pose detection results with the Single Shot MultiBox Detector (SSD) algorithm, You Only Look Once (YOLOV3) algorithm, RetinaNet algorithm, and Faster_R-CNN algorithm. Preliminary tests show that the method proposed in this paper achieves a 0.0128 standard deviation of precision and 0.9218 ± 0.0048 of confidence (95%) and a 0.0266 standard deviation of recall and 0.8996 ± 0.0099 of confidence (95%). By successfully estimating the pose of broiler chickens, it is possible to facilitate the detection of abnormal behavior of poultry. Furthermore, the method can be further improved to increase the overall success rate of verification.