融合数据增强与改进ResNet34的奶牛热红外图像乳腺炎检测

乳腺炎是奶牛生产养殖中最为严重的疾病之一，奶牛乳腺炎的早期检测可以为后续治疗提供依据，从而提高疾病治疗效率，降低养殖风险。为了对自然行走的奶牛实现快速、高精度的“一步式”乳腺炎疾病检测，提出了一种基于热红外图像，融合数据增强与改进ResNet34的奶牛乳腺炎疾病检测方法。相对于现有的“多步式”奶牛红外图像乳腺炎检测方法，该方法无需奶牛关键部分如乳房和眼睛的定位以及温度提取等，可有效避免“多步式”造成的误差累计，从而实现更高效的乳腺炎检测。首先，将包含奶牛关键部位的局部图片水平拼接成信息完整的整体图片，结合RandAugment数据增强方法扩增训练样本；其次，采用ResNet34残差网络作为实验的基础网络，并根据热红外图像特性对模型进行如下改进：（1）精简网络内部冗余层使得模型更轻量化；（2）中间层添加辅助分类器弥补由于模型精简带来的特征损失；（3）将改进的多融合池化层代替原有单一池化层，使得特征提取内容更丰富。随机选取3 298张热红外图像（66头奶牛）作为实验对象，并设置多组对比实验，结果表明: 与传统ResNet34相比改进后ResNet34模型分类准确率提高3.4%，基于改进ResNet34并融合迁移学习和数据增强的模型验证准确率达到90.3%，测试准确率为88.4%，分类时间仅需3.39×10-3 s。为了保证实验数据集的样本独立性，进一步将奶牛个体数量按照3∶1∶1划分为训练集、验证集和测试集，测得模型测试准确率达到80.3%，证明所提出模型具有很好的鲁棒性。根据测试结果，计算出模型查准率为91.2%、查全率为91.6%、F1分数为91.4%，与前人所做实验相比准确率提高了5.1%，特异度提升5.3%。该研究方法可以为初期奶牛乳腺疾病筛选和医学诊断提供辅助和参考。

Detection of Dairy Cow Mastitis From Thermal Images by Data Enhancement and Improved ResNet34

Mastitis is one of the most serious diseases in dairy production and breeding. The early detection of cow mastitis can provide the basis for follow-up treatment to improve the efficiency of disease treatment and reduce the risk of breeding. In order to realize fast and high-precision “one-step” mastitis disease detection for naturally walking dairy cows, a dairy cow mastitis disease detection method based on the thermal infrared image, data enhancement and improved ResNet34 is proposed in this paper. Compared with the existing “multi-step” dairy cow infrared image mastitis detection method, this method does not need the positioning of key parts of dairy cows, such as breast and eyes and temperature extraction, which can effectively avoid the error accumulation caused by “multi-step”, to achieve more efficient mastitis detection. Firstly, this method horizontally splices the local pictures containing the key parts of the cow into an overall picture with complete information and expands the training samples combined with the RandAugment data enhancement method; Secondly, the ResNet34 residual network is used as the basic network of the experiment, and the model is improved as follows according to the characteristics of thermal infrared image: (1) simplify the redundant internal layer of the network to make the model lighter; (2) Auxiliary classifiersare added in the middle layer to make up for the feature loss caused by model simplification; (3) The improved multi fusion pool layer is used to replace the original single pool layer, which makes the content of feature extraction richer. Finally, 3 298 thermal infrared images (66 cows) were randomly selected as the experimental objects, and multiple groups of comparative experiments were set. The results showed that compared with the traditional ResNet34, the classification accuracy of the improved ResNet34 model was improved by 3.4%, the model verification accuracy based on the improved ResNet34 combined with transfer learning and data enhancement was 90.3%, the test accuracy was 88.4%, and the classification time was only 3.39×10-3 seconds. In addition, to ensure theindependence of the experimental data set, this paper further divides it into the training set, verification set and test set according to the number of dairy cows in 3∶1∶1. The test accuracy of the model was 80.3%, which proves that the proposed model has good robustness. According to the test results, it is calculated that the precision rate, recall rate and F1 score of the model are 91.2%, 91.6% and 91.4%. Compared with previous experiments, the accuracy is improved by 5.1% and the specificity is improved by 5.3%. In conclusion, this research method can provide a reference for screening and medical diagnosis of breast diseases in early dairy cows.