  You Only Look Once (YOLO) is a single-stage object detection model popular
for real-time object detection, accuracy, and speed. This paper investigates
the YOLOv5 model to identify cattle in the yards. The current solution to
cattle identification includes radio-frequency identification (RFID) tags. The
problem occurs when the RFID tag is lost or damaged. A biometric solution
identifies the cattle and helps to assign the lost or damaged tag or replace
the RFID-based system. Muzzle patterns in cattle are unique biometric solutions
like a fingerprint in humans. This paper aims to present our recent research in
utilizing five popular object detection models, looking at the architecture of
YOLOv5, investigating the performance of eight backbones with the YOLOv5 model,
and the influence of mosaic augmentation in YOLOv5 by experimental results on
the available cattle muzzle images. Finally, we concluded with the excellent
potential of using YOLOv5 in automatic cattle identification. Our experiments
show YOLOv5 with transformer performed best with mean Average Precision (mAP)
0.5 (the average of AP when the IoU is greater than 50%) of 0.995, and mAP
0.5:0.95 (the average of AP from 50% to 95% IoU with an interval of 5%) of
0.9366. In addition, our experiments show the increase in accuracy of the model
by using mosaic augmentation in all backbones used in our experiments.
Moreover, we can also detect cattle with partial muzzle images.
