Abstract Mastitis is a disease that affects dairy cow health, and the timely detection of mastitis can improve the efficiency of mastitis treatment and reduce economic losses in the milk industry. To improve the detection speed and achieve automatic recognition of dairy cow mastitis, this study proposed a deep learning network EFMYOLOv3 (Enhanced Fusion MobileNetV3 You Only Look Once v3) based on the bilateral filtering enhancement of thermal images. EFMYOLOv3 is used to automatically detect dairy cow eyes and udders and is applied to the detection of mastitis in dairy cows based on thermal infrared images. We proposed a bilateral filtering image enhancement algorithm based on gray histograms to enhance image details to compensate for weak thermal image details and enhance the contrast between the foreground and background. We chose the lightweight MobileNetV3 as the backbone of YOLOv3. Based on the location attention mechanism, we used the multiscale enhanced fusion feature pyramid network structure as the feature extraction module. The feature map used for prediction was designed with the appropriate resolution and powerful multilayer semantic features to improve the accuracy of target detection. We replaced the standard convolutions in the base layer with depthwise separable convolutions to reduce the number of learning parameters. To verify the effectiveness of the target detection algorithm, the accuracy, recall, average frame rate, average accuracy and other indicators were compared with the SSD (single shot multibox detector) and YOLOv3 (You Only Look Once v3) algorithms. The test results revealed that the average frame rate of the EFMYOLOv3 algorithm is 99 frames per second (fps), and the average accuracy is 96.8%, which means that the key parts of the cow can be detected quickly and accurately. The temperature difference between the eyes and the udders was obtained by the target detection algorithm, and the mastitis detection of dairy cows was performed and compared with the somatic cell count (SCC). The results showed that the accuracy of the mastitis classification algorithm is 83.33%, and the sensitivity and specificity are 92.31% and 76.47%, respectively. This method realized accurate positioning of key parts of dairy cows and can be used for the automatic recognition of dairy cow mastitis.

中文翻译：

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基于深度学习检测器的热图像自动识别奶牛乳腺炎

摘要 乳腺炎是影响奶牛健康的疾病，及时发现乳腺炎可以提高乳腺炎治疗效率，减少奶业经济损失。为了提高检测速度并实现奶牛乳腺炎的自动识别，本研究提出了一种基于热图像双边滤波增强的深度学习网络EFMYOLOv3（Enhanced Fusion MobileNetV3 You Only Look Once v3）。EFMYOLOv3用于自动检测奶牛的眼睛和乳房，应用于基于热红外图像的奶牛乳腺炎检测。我们提出了一种基于灰度直方图的双边滤波图像增强算法来增强图像细节，以补偿微弱的热图像细节，增强前景和背景之间的对比度。我们选择轻量级的 MobileNetV3 作为 YOLOv3 的主干。基于位置注意力机制，我们使用多尺度增强融合特征金字塔网络结构作为特征提取模块。用于预测的特征图设计有适当的分辨率和强大的多层语义特征，以提高目标检测的准确性。我们用深度可分离卷积替换了基础层中的标准卷积，以​​减少学习参数的数量。为验证目标检测算法的有效性，将准确率、召回率、平均帧率、平均准确率等指标与SSD（单发多框检测器）和YOLOv3（You Only Look Once v3）算法进行对比。测试结果显示，EFYOLOv3算法的平均帧率为每秒99帧（fps），平均准确率为96.8%，这意味着可以快速准确地检测出奶牛的关键部位。通过目标检测算法获得眼睛和乳房之间的温差，对奶牛进行乳腺炎检测，并与体细胞计数（SCC）进行比较。结果表明，乳腺炎分类算法的准确率为83.33%，敏感性和特异性分别为92.31%和76.47%。该方法实现了奶牛关键部位的准确定位，可用于奶牛乳腺炎的自动识别。这意味着可以快速准确地检测奶牛的关键部位。通过目标检测算法获得眼睛和乳房之间的温差，对奶牛进行乳腺炎检测，并与体细胞计数（SCC）进行比较。结果表明，乳腺炎分类算法的准确率为83.33%，敏感性和特异性分别为92.31%和76.47%。该方法实现了奶牛关键部位的准确定位，可用于奶牛乳腺炎的自动识别。这意味着可以快速准确地检测奶牛的关键部位。通过目标检测算法获得眼睛和乳房之间的温差，对奶牛进行乳腺炎检测，并与体细胞计数（SCC）进行比较。结果表明，乳腺炎分类算法的准确率为83.33%，敏感性和特异性分别为92.31%和76.47%。该方法实现了奶牛关键部位的准确定位，可用于奶牛乳腺炎的自动识别。结果表明，乳腺炎分类算法的准确率为83.33%，敏感性和特异性分别为92.31%和76.47%。该方法实现了奶牛关键部位的准确定位，可用于奶牛乳腺炎的自动识别。结果表明，乳腺炎分类算法的准确率为83.33%，敏感性和特异性分别为92.31%和76.47%。该方法实现了奶牛关键部位的准确定位，可用于奶牛乳腺炎的自动识别。