Computer Vision on the Edge: Individual Cattle Identification in Real-Time With ReadMyCow System

Moniek Smink, Haotian Liu, Dörte Döpfer, Yong Jae Lee; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 7056-7065

In precision livestock farming, the individual identification of cattle is crucial to inform the decisions made to enhance animal welfare, health, and productivity. In literature, models exist that can read ear tags; however, they are not easily portable to real-world cattle production environments and make predictions mainly on still images. We propose a video-based cattle ear tag reading system, called ReadMyCow, which takes advantage of the temporal characteristics in videos to accurately detect, track, and read cattle ear tags at 25 FPS on edge devices. For each frame in a video, ReadMyCow functions in two steps. 1) Tag detection: a YOLOv5s Object Detection model and NVIDIA Deepstream Tracking Layer detect and track the tags present. 2) Tag reading: the novel WhenToRead module decides whether to read each tag, using a TRBA Scene Text Recognition model, or to use the reading from a previous frame. The system is implemented on an edge device, namely the NVIDIA Jetson AGX Orin or Xavier, making it portable to cattle production environments without external computational resources. To attain real-time speeds, ReadMyCow only reads the detected tag in the current frame if it thinks it will get a better reading when a decision metric is significantly improved in the current frame. Ideally, this means the best reading of a tag is found and stored throughout a tag's presence in the video, even when the tag becomes occluded or blurry. While testing the system at a real Midwestern dairy farm housing 9,000 cows, 96.1% of printed ear tags were accurately read by the ReadMyCow system, demonstrating its real-world commercial potential. ReadMyCow opens opportunities for informed data-driven decision-making processes on commercial cattle farms.