A deep learning approach combining DeepLabV3+ and improved YOLOv5 to detect dairy cow mastitis

Abstract

Dairy cow mastitis has a great impact on the productivity of dairy cows and the profits of livestock farms. Early detection is of great significance to improve the efficiency of mastitis treatment. However, due to the low resolution of thermal infrared images and the complexity of the living environment of dairy cows, it is difficult to detect the eyes and udders of cows, which reduces the detection accuracy of mastitis. To solve this problem, this paper proposes a two-stage model (DCYOLO) integrating the DeepLabV3 + semantic segmentation network and an improved YOLOv5 target recognition network, which is used to detect the eyes and udders of dairy cows under complex background and applied to the severity classification of dairy cow mastitis. In the first stage, the DeepLabV3 + model was used to segment the eyes and udders of dairy cows from thermal infrared images. In the second stage, the segmented image was input into the target recognition YOLOv5 network for key parts recognition. Finally, to further improve the detection accuracy of the model, a convolutional block attention module (CBAM) was added at the end of the main part of the YOLOv5 model. After comparing different semantic segmentation and target recognition networks, the DeepLabV3 + network and YOLOv5 network performed best. The mIoU and mean pixel accuracy (mPA) of the DeepLabV3 + network reached 86.98 % and 92.92 %, respectively. The mean average precision (mAP) and F1 scores of the YOLOv5 network for unsegmented thermal infrared images reached 93.4 % and 90.9 %, respectively. The CBAM-added YOLOv5 (CAYOLOv5) model was combined with the DeepLabV3 + model. Compared with the single YOLOv5 model, the mAP and F1 scores of DCYOLO increased by 5.4 % and 5.3 %, respectively. Therefore, the proposed model can achieve more accurate positioning of key parts of dairy cows. Based on this model, the eye and udder temperature differences of 50 dairy cows were extracted for mastitis detection, and the detection results were compared with the results of the somatic cell count (SCC) approach. The results showed that the classification accuracy of mastitis was 86 %, and the average sensitivity and specificity were 79.41 % and 92.49 %, respectively. The dairy cow mastitis detection method based on the two-stage model can accurately locate the key parts of dairy cows and realize the automatic detection and classification of dairy cow mastitis, and the accuracy is high.