黑龙江畜牧兽医Issue(6)：63-68,6.DOI:10.13881/j.cnki.hljxmsy.2025.10.0103
基于改进YOLOv10n模型的牛脸识别方法研究
Study of a cattle face recognition method based on an improved YOLOv10n model
摘要
Abstract
In order to solve the limited accuracy of conventional deep learning models in cattle face recognition,YOLO-SRC,an enhanced version of YOLOv10n was proposed.Introducing the SPPF_SHViT module into the YOLOv10n model could enhance feature extraction capability.By using the C2f_RepViT module to optimize feature representation and embedding the CLA attention mechanism to form the PSA_CA module,adaptive multi-scale feature fusion was achieved.The results showed that in the Huaxi cattle face dataset,YOLO-SRC achieved an average precision mean value(mAP)of 93.6%,with an accuracy level of 93.4%,and a recall rate of 83.2%,which were 2.9,2.0,and 3.5 percentage higher than those of YOLOv10n,respectively.Compared with Faster R-CNN,YOLOv7,YOLOv8n,and YOLOv11n,the heatmap results of the YOLO-SRC model showed that it could better focus on facial regions and had the highest confidence,indicating that the YOLO-SRC model had good recognition performance and robustness in cattle face recognition.关键词
牛脸识别/深度学习/YOLO-SRC/性能/华西牛Key words
cattle face recognition/deep learning/YOLO-SRC/performance/Huaxi cattle分类
农业科技引用本文复制引用
何氽,郭鹏,朱波..基于改进YOLOv10n模型的牛脸识别方法研究[J].黑龙江畜牧兽医,2026,(6):63-68,6.基金项目
国家自然科学基金项目(32272843) （32272843）
甘肃省科技计划资助项目(26CXNM002) （26CXNM002）
中国农业科学院委托项目"牛脸识别系统设计开发"(TNHXKJ2023037) （TNHXKJ2023037）
