  The management of cattle over a huge area is still a challenging problem in
the farming sector. With evolution in technology, Unmanned aerial vehicles
(UAVs) with consumer level digital cameras are becoming a popular alternative
to manual animal censuses for livestock estimation since they are less risky
and expensive.This paper evaluated and compared the cutting-edge object
detection algorithms, YOLOv7,RetinaNet with ResNet50 backbone, RetinaNet with
EfficientNet and mask RCNN. It aims to improve the occlusion problem that is to
detect hidden cattle from a huge dataset captured by drones using deep learning
algorithms for accurate cattle detection. Experimental results showed YOLOv7
was superior with precision of 0.612 when compared to the other two algorithms.
The proposed method proved superior to the usual competing algorithms for cow
face detection, especially in very difficult cases.
