Behavior recognition and multi-object tracking of dairy cows in free-style cow barn environments play a crucial role in monitoring their health status and serve as an essential means for intelligent scientific farming. This study proposes an efficient embedded algorithm, MTW-BYTE, for dairy cow behavior recognition and multi-object tracking. It addresses challenges in free-style cow barn environments, including the impact of lighting variations and common occlusions on behavior recognition, as well as trajectory interruptions and identity ID switching during multi-object tracking. First, the MTW-YOLO cow behavior recognition model is constructed based on the YOLOv11n object detection algorithm. Replacing parts of the backbone network and neck network with MANet and introducing the Task Dynamic Align Detection Head (TDADH). The CIoU loss function of YOLOv11n is replaced with the WIoU loss. The improved model not only effectively handles variations in lighting conditions but also addresses common occlusion issues in cows, enhancing multi-scale behavior recognition capabilities and improving overall detection performance. The improved MTW-YOLO algorithm improves Precision, Recall, mAP50 and F1 score by 4.5%, 0.1%, 1.6% and 2.2%, respectively, compared to the original YOLOv11n model. Second, the ByteTrack multi-object tracking algorithm is enhanced by designing a dynamic buffer and re-detection mechanism to address cow trajectory interruptions and identity ID switching. The MTW-YOLO algorithm is cascaded with the improved ByteTrack to form the multi-target tracking algorithm MTW-BYTE. Compared with the original multi-target tracking algorithm YOLOv11n-ByteTrack (a combination of YOLOv11n and the original ByteTrack), this algorithm improves HOTA by 1.1%, MOTA by 3.6%, MOTP by 0.2%, and IDF1 by 1.9%, reduces the number of ID changes by 11, and achieves a frame rate of 43.11 FPS, which can meet the requirements of multi-target tracking of dairy cows in free-style cow barn environments. Finally, to verify the model’s applicability in real-world scenarios, the MTW-BYTE algorithm is deployed on an NVIDIA Jetson AGX Orin edge device. Based on real-time monitoring of cow behavior on the edge device, the pure inference time for a single frame is 16.62 ms, achieving an FPS of 29.95, demonstrating efficient and stable real-time behavior detection and tracking. The ability of MTW-BYTE to be deployed on edge devices to identify and continuously track cow behavior in various scenarios provides hardware feasibility verification and algorithmic support for the subsequent deployment of intelligent monitoring systems in free-style cow barn environments.
Wu et al. (Sun,) studied this question.