Cow body condition score is an important indicator to evaluate the productivity and physical health of cows. At present, with the development of modern farming, intelligent detection technology has been applied to precision farming of dairy cows. In view of the problems of large number of parameters and large calculation of the current detection algorithm, an improved lightweight cow body condition scoring model (YOLO-MCE) was proposed based on YOLO v5s. Firstly, a 2D camera was used to acquire the cow tail images at the cow milking lane, and those images were filtered to obtain the final BCS dataset. Secondly, the coordinate attention (CA) mechanism was integrated into the MobileNetV3 network to build the M3CA network, which was used to replace the YOLO v5s backbone network to reduce the complexity of the model, and make the network feature extraction pay more attention to the location and spatial information of the cow tail area. Finally, the EIoU Loss function was used in the prediction layer of YOLO v5s to optimize the regression convergence speed of the target bounding box and generate a prediction bounding box with accurate positioning. The experimental results showed that the improved YOLO v5s model had a detection precision of 93.4%, a recall rate of 85.5%, an mAP@0.5 of 91.4%, a FLOPs of 2.0×109, and a model size of 2.28MB. Compared with the original YOLO v5s model, the FLOPs and model size of YOLO-MCE were reduced by 87.3% and 83.4%, respectively, which further showed that the proposed method can achieve efficient scoring of cow body conditions under the condition of low model complexity and high real-time performance. In addition, the overall performance of the improved YOLO v5s model was superior to that of the Fast R-CNN, SDD and YOLO v3 object detection models. The research result can provide a theoretical basis and research ideas for the commercialization of dairy cow body condition scoring, and offer a research direction for the application of intelligent algorithms.