Affiliations: School of Electronic Information Engineering, Inner Mongolia University, Inner Mongolia, PR China
Correspondence:
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Corresponding author. Xuemei Lei, School of Electronic Information Engineering, Inner Mongolia University, Inner Mongolia, PR China. E-mail: [email protected].
Abstract: The occlusion in the real feedlot environment is ubiquitous, and the current research based on the cattle face recognition under occlusion conditions is almost non-existent. Thus, an attention mechanism module with high accuracy and low model complexity is designed to incorporate into MobileNet so that the cattle face under occlusion can be identify accurately, which is the RGB images captured in the ranch environment. In this paper, we also construct a Simmental cattle face image dataset for data modeling and method evaluation, which contains 10,239 images of 103 cattle. The experimental results show that when the occluder is in the upper left and lower right corner, if the occlusion rate is less than 30%, the value of Top_1 reaches more than 90%; if it is less than 50%, the value of Top_1 is more than 80%. Even if the middle part occludes lots of important information, the occlusion rate of 40% has an accuracy of more than 80%. Furthermore, comparing the proposal model with MobileNet, the parameter and model size are equal, and the amount of calculation as a cost increase a little. Therefore, the proposal model is suitable to transplant to the embedded system in the future.
