Identifying the Mating Posture of Cattle Using Deep Learning-Based Object Detection with Networks of Various Settings

Abstract

Estrus detection in cattle is an important factor in livestock farming. With timely estrus detection, cattle are artificially fertilized and isolated for safety, which directly affects the productivity of livestock farms. Estrus can be successfully detected by identifying the mating posture of cattle. Therefore, in this paper, we propose the identification of cattle mating posture based on video inputs for prompt estrus detection. A deep learning-based object detection network that focuses on real-time processing with high processing speeds is applied. The use of deep learning-based object detection shows high accuracy, even with noise robustness. The performance of the network is improved through the inclusion of an additional layer and a new activation function. The composition of the additional layer enables training by extracting more features required for object detection. The application of the new activation function, Mish, which has a smoother curve, allows for better generalization and improves the accuracy of the results. The data needed for training were gathered by installing cameras at a livestock farm, and various datasets were used depending on camera placement. The results of this study were verified by the evaluation of four networks using test datasets containing image and video data from different environments. The identification of the mating posture of cattle attained 98.5% precision, 97.2% recall, and 97.8% accuracy.