农业机械学报2025，Vol.56Issue(6)：237-246,10.DOI:10.6041/j.issn.1000-1298.2025.06.023
基于改进YOLO v11n-seg的奶牛乳房炎检测方法
Detection Method for Cow Mastitis Based on Improved YOLO v11n-seg
摘要
Abstract
When dairy cows suffer from mastitis,the temperature difference between their udder surface and eye surface is relatively large.Therefore,the temperature difference between the udder and eye can be used as an indicator for mastitis detection.To address the issues of low accuracy,false detections,and missed detections in existing thermal infrared image-based mastitis detection methods,an improved YOLO v11n-seg method for mastitis detection in dairy cows was proposed,which utilized both thermal infrared and visible light registered images.To achieve more precise segmentation of the cow's udder and eyes under limited computational resources,targeted improvements were made.Firstly,the ADown convolution module was used to replace some of the ordinary convolution layers in the baseline model(YOLO v11n-seg)for efficient feature extraction,which was beneficial for model deployment and usage in resource-constrained environments.Secondly,the MLCA attention mechanism was introduced at the end of the backbone network,significantly enhancing the feature extraction capability for small-scale objects.Finally,the RepGFPN structure was adopted in the neck network to optimize feature fusion and information transmission,further improving segmentation accuracy.The improved YOLO v11n-seg model achieved an average segmentation accuracy of 90.3％for cow eyes and 97.9％for udders.Compared with the baseline model,the improved YOLO v11n-seg model increased the average segmentation accuracy by 7.1 percentage points and 0.7 percentage points,respectively,while reducing the number of model parameters by 14.3％and the computational cost by 12.5％.The temperature difference between the udder and eye,extracted from the segmentation mask and temperature matrix,was compared with the set temperature difference threshold and verified by the somatic cell count method.The results showed that the accuracy of mastitis detection in dairy cows reached 88.46％.This proved that the proposed method can achieve udder and eye segmentation in dairy cows and can be applied to mastitis detection.关键词
奶牛乳房炎/实例分割/YOLO v11/轻量化/智慧养殖Key words
cow mastitis/instance segmentation/YOLO v11/lightweight/smart farming分类
信息技术与安全科学引用本文复制引用
田文斌,姚渝,吕昊暾,杜瑞杰,王宁..基于改进YOLO v11n-seg的奶牛乳房炎检测方法[J].农业机械学报,2025,56(6):237-246,10.基金项目
国家重点研发计划项目(2022YFC2304004) （2022YFC2304004）
