Study on Body Size Measurement Method of Goat and Cattle under Different Background Based on Deep Learning

Abstract

The feasibility of using depth sensors to measure the body size of livestock has been extensively tested. Most existing methods are only capable of measuring the body size of specific livestock in a specific background. In this study, we proposed a unique method of livestock body size measurement using deep learning. By training the data of cattle and goat with same feature points, different animal sizes can be measured under different backgrounds. First, a novel penalty function and an autoregressive model were introduced to reconstruct the depth image with super-resolution, and the effect of distance and illumination on the depth image was reduced. Second, under the U-Net neural network, the characteristics exhibited by the attention module and the DropBlock were adopted to improve the robustness of the background and trunk segmentation. Lastly, this study initially exploited the idea of human joint point location to accurately locate the livestock body feature points, and the livestock was accurately measured. According to the results, the average accuracy of this method was 93.59%. The correct key points for detecting the points of withers, shoulder points, shallowest part of the chest, highest point of the hip bones and ischia tuberosity had the percentages of 96.7%, 89.3%, 95.6%, 90.5% and 94.5%, respectively. In addition, the mean relative errors of withers height, hip height, body length and chest depth were only 1.86%, 2.07%, 2.42% and 2.72%, respectively.