Deep Multi-task Learning for Animal Chest Circumference Estimation from Monocular Images

The applications of deep learning algorithms with images to various scenarios have attracted significant research attention. However, application scenarios in animal breeding managements are still limited. In this paper we propose a new deep learning framework to estimate the chest circumference of domestic animals from images. This parameter is a key metric for breeding and monitoring the quality of animal in animal husbandry. We design a set of feature extraction methods based on a multi-task learning framework to address the challenging issues in the main estimation task. The multiple tasks in our proposed framework include object segmentation, keypoint estimation, and depth estimation of cow from monocular images. The domain-specific features extracted from these tasks improve upon our main estimation task. In addition, we also attempt to reduce unnecessary computations during the framework design to reduce the cost of subsequent practical implementation of the developed system. Our proposed framework is tested on our own collected dataset to evaluate its performance.