SOLOv2-based multi-view contactless bovine body size measurement

Abstract

To address the problems that traditional bovine body measurement methods require a lot of manual assistance and lead to stress reactions in cattle, this paper achieves contactless measurement of bovine body length, withers height, chest breath, belly breath and chest depth by using a deep learning approach. This paper use SOLOv2 instance segmentation to identify cattle and extract cattle contours from the top and side views, combines cattle image dataset and OpenCV image processing function to extract cattle feature parts, and use discrete curvature calculation method to extract cattle body size to calculate feature points, and calculate cattle body size parameters by Euclidean distance calculation method. Experiments were conducted using custom model cattle to which bovine body size measurements were taken, after comparing with the manual measurement results, the average relative errors of body length, body height, chest depth, chest breath and belly breath of the model cattle were 1.36%, 0.44%, 2.05%, 2.80% and 1.47%, respectively. The experiment proved that this measurement method performed well in the non-contact measurement of bovine body size and had good accuracy, which provided a new way and method for the measurement of the non-stress response of cattle.