农业机械学报2026，Vol.57Issue(13)：347-358,12.DOI:10.6041/j.issn.1000-1298.2026.13.029
多阶段自适应增强的奶牛后乳房性状分割方法
Multi-stage Adaptive Activation Enhancement Segmentation Method for Dairy Cow Rear Udder Traits
摘要
Abstract
Cow rear udder traits are key indicators for evaluating dairy cows'production performance and breeding value,and their accurate and automated evaluation is of great significance for improving dairy farm management efficiency and genetic breeding levels.The complex structural morphology,naturally blurred boundaries of cow rear udders,as well as interferences such as occlusion and variable lighting in milking sites,make high-precision and automated image segmentation and trait evaluation extremely challenging.A multi-stage adaptive enhancement segmentation network for cow rear udder traits(MAAE-SegNet)was proposed.By introducing an adaptive parameter activation mechanism,it enhanced the backbone network's dynamic expression capability for udder features in complex scenarios,and constructed a dynamic gated attention module to effectively focus on the key regions of the rear udder,thereby improving the clarity and completeness of rear udder segmentation boundaries.Experimental results showed that compared with the Mask2Former model,the improved model achieved 0.5 and 1.5 percentage points improvements in detection box accuracy and recall rate respectively,and 1.8 and 2.0 percentage points improvements in segmentation accuracy and recall rate respectively.The model had a parameter count of 4.705 5×107 and a floating-point operation(FLOP)count of 1.59×1011,demonstrating higher accuracy without a significant increase in parameter quantity.关键词
奶牛后乳房/图像分割/注意力机制/线性评分Key words
dairy cow rear udder/image segmentation/attention mechanism/linear scoring分类
信息技术与安全科学引用本文复制引用
李旭文,高荣华,李奇峰,王荣,坎杂,何盈盈,戎玉娇,周杰,张俊..多阶段自适应增强的奶牛后乳房性状分割方法[J].农业机械学报,2026,57(13):347-358,12.基金项目
北京市自然科学基金面上项目(43242037)和北京市农林科学院探索项目(TSXM202511) （43242037）
