Automatic recognition of dairy cow mastitis from thermal images by a deep learning detector

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.