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:

奶牛后乳房性状是评估奶牛生产性能与育种价值的关键指标,其精准自动化评定对提升牛场管理效率以及遗传选育水平具有重要意义。针对奶牛后乳房结构形态复杂、边界自然模糊,且挤奶现场存在遮挡、光照多变等干扰,使得对其进行高精度、自动化的图像分割与性状评定困难问题,本文提出了一种多阶段自适应增强的奶牛后乳房性状分割模型(MAAE-SegNet)。通过引入自适应参数激活机制,增强骨干网络对复杂场景中乳房特征的动态表达能力,并构建动态门控注意力模块以有效聚焦后乳房关键区域,提升后乳房分割边界的清晰度与完整性。试验结果表明,与Mask2Former模型相比,本文改进后模型的检测框精确率、召回率分别提升0.5、1.5个百分点,分割精确率与召回率分别提升1.8、2.0个百分点。其中模型参数量为4.7055×107,浮点数运算量为1.59×1011,改进模型在参数量不激增情况下精度更高。

Abstract:

Cow rear udder traits are key indicators for evaluating dairy cows’ roduction 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.