Key Lab of Modern Precision Agriculture System Integration Research, Ministry of Education of China, China Agricultural University, Beijing, P.R. China 100083; Key Lab of Agricultural Information Acquisition Technology, Ministry of Agricultural of China, China Agricultural University, Beijing, P.R. China 100083.
Key Lab of Modern Precision Agriculture System Integration Research, Ministry of Education of China, China Agricultural University, Beijing, P.R. China 100083; Key Lab of Agricultural Information Acquisition Technology, Ministry of Agricultural of China, China Agricultural University, Beijing, P.R. China 100083.
J Dairy Sci. 2020 Nov;103(11):10628-10638. doi: 10.3168/jds.2020-18288. Epub 2020 Sep 18.
Lameness has a considerable influence on the welfare and health of dairy cows. Many attempts have been made to develop automatic lameness detection systems using computer vision technology. However, these detection methods are easily affected by the characteristics of individual cows, resulting in inaccurate detection of lameness. Therefore, this study explores an individualized lameness detection method for dairy cattle based on the supporting phase using computer vision. This approach is applied to eliminate the influence of the characteristics of individual cows and to detect lame cows and lame hooves. In this paper, the correlation coefficient between lameness and the supporting phase is calculated, a lameness detection algorithm based on the supporting phase is proposed, and the accuracy of the algorithm is verified. Additionally, the reliability of this method using computer vision technology is verified based on deep learning. One hundred naturally walking cows are selected from video data for analysis. The results show that the correlation between lameness and the supporting phase was 0.864; 96% of cows were correctly classified, and 93% of lame hooves were correctly detected using the supporting phase-based lameness detection algorithm. The mean average precision is 87.0%, and the number of frames per second is 83.3 when the Receptive Field Block Net Single Shot Detector deep learning network was used to detect the locations of cow hooves in the video. The results show that the supporting phase-based lameness detection method proposed in this paper can be used for the detection and classification of cow lameness and the detection of lame hooves with high accuracy. This approach eliminates the influence of individual cow characteristics and could be integrated into an automatic detection system and widely applied for the detection of cow lameness.
跛行对奶牛的福利和健康有很大的影响。许多人尝试利用计算机视觉技术开发自动跛行检测系统。然而，这些检测方法很容易受到个体奶牛特征的影响，导致跛行检测不准确。因此，本研究探索了一种基于计算机视觉的奶牛个体跛行检测方法。该方法应用于消除个体奶牛特征的影响，检测跛行奶牛和跛行蹄。本文计算了跛行与支撑阶段的相关系数，提出了一种基于支撑阶段的跛行检测算法，并验证了算法的准确性。此外，还基于深度学习验证了计算机视觉技术的这种方法的可靠性。从视频数据中选择了 100 头自然行走的奶牛进行分析。结果表明，跛行与支撑阶段的相关性为 0.864；支撑阶段跛行检测算法可正确分类 96%的奶牛，正确检测 93%的跛行蹄。使用 Receptive Field Block Net Single Shot Detector 深度学习网络检测视频中奶牛蹄部位置时，平均精度为 87.0%，每秒帧数为 83.3。结果表明，本文提出的基于支撑阶段的跛行检测方法可用于奶牛跛行的检测和分类以及跛行蹄的检测，具有较高的准确性。该方法消除了个体奶牛特征的影响，可以集成到自动检测系统中，广泛应用于奶牛跛行检测。
