Abstract: Lameness is an abnormal gait or stance of an animal, indicating a more severe disorder of the locomotion system. The disturbance in the gait and body has posed a great threat to the welfare, health, and production of dairy cows in the herds. An early observation is typically performed on the sudden change in gait during day-to-day dairy practices. However, the current detection of lameness was widely used in the 2D cow video, particularly susceptible to light and environmental inferences. In this study, a novel approach was proposed to extract the movement features of the lameness cows using thermal infrared video under deep learning and image processing, in order to improve the detection accuracy and robustness against the complex illumination and environmental disturbances. Taking the multiparous lactating Holstein cows as the study objects, the datasets were collected at the Dingyuan farm in Hebei Province of China in September 2020. The gait features of lameness were related to the head, hoof, back, shoulder, and hip of cows. The performances of different target detection were then evaluated on the dataset of locomotion features from the thermal infrared and 2D images of cows walking. The results showed that the average accuracies of deep learning for the detection of cows locomotion features were 90.84%, 86.68%, and 79.68%, respectively, in the thermal infrared, daylight, and daylight images, whereas, those in the traditional image processing were 74.26%, 54.31%, and 48.95%, respectively. The test results demonstrated that the target detection of deep learning and image processing performed better on the thermal infrared images, compared with the 2D images. The thermal infrared images had effectively reduced the influences of external factors, such as the light and background. In addition, a lameness detection test was conducted on the walking cows using the thermal infrared video and 2D video, according to the arched curvature. It was found that the detection accuracies of cow lameness in the thermal infrared and 2D video were 90.0% and 83.3%, respectively, where the values of Macro-F1 were 0.90 and 0.83, respectively. Consequently, a better performance was achieved for the application of thermal infrared cameras in cow lameness detection using computer vision. This finding can provide a new potential strategy for the timely diagnosis and prevention of abnormal gait in cows in the dairy farming industry.