农业机械学报2024，Vol.55Issue(11)：93-102,10.DOI:10.6041/j.issn.1000-1298.2024.11.010
基于特征掩膜的局部遮挡牛脸识别方法
Feature Mask-based Local Occlusion Cattle Face Recognition Method
摘要
Abstract
With the rapid development of intelligent animal husbandry,bovine face recognition has become the key to intelligent cattle breeding,but the problem of bovine face occlusion in practical application scenarios is more serious,which brings challenges to the performance of the recognition system.To solve this problem,a two-branch network structure based on occluder-assisted bovine face recognition was proposed.Firstly,an improved lightweight U-Net occlusion segmentation model was designed.By adding deep separable convolution and multi-scale mixing pool module,the occlusion segmentation performance of the segmentation network was effectively improved.Secondly,in order to better attenuate the influence of occlusions on bovine face recognition performance,a multilevel mask generation unit was introduced,and masks corresponding to different stages of the recognition network were constructed with different levels of occlusions as input.The damaged feature information caused by occlusions was effectively eliminated in each stage of feature extraction through mask operation.Finally,for the validity and real-time performance of the detection algorithm,the algorithm was verified on the self-made data set,and compared with a variety of recent typical recognition algorithms.The experimental results showed that the proposed algorithm had an average accuracy of 86.34％on the blocked cow face data set,and the recognition speed was 54 f/s.Compared with the single-scale mask,the average accuracy of multistage mask was improved by 2.02 percentage points,and the recognition effect was better than that of the comparison network under different degrees of occlusion.关键词
遮挡牛脸识别/图像分割/多级掩膜学习/双分支结构Key words
occluded cattle face recognition/image segmentation/multi-level mask learning/dual-branch structure分类
信息技术与安全科学引用本文复制引用
齐咏生,张新泽,张嘉英,刘利强,李永亭..基于特征掩膜的局部遮挡牛脸识别方法[J].农业机械学报,2024,55(11):93-102,10.基金项目
国家自然科学基金项目(62363029)、内蒙古科技计划项目(2021GG164)和内蒙古自然科学基金项目(2022MS06018、2021MS06018) （62363029）
