论文标题
牛检测闭塞问题
Cattle Detection Occlusion Problem
论文作者
论文摘要
在巨大地区，牛的管理仍然是农业部门的一个挑战性问题。随着技术的发展，带有消费者级数码相机的无人机（UAV）正成为手动动物普查的流行替代品以进行牲畜估算，因为它们的风险较小且昂贵。对本文进行了评估并比较了与Resnet50 backnet50 backnetnetnetnnetn和Otticnetn的尖端对象检测算法，YOLOV7，YOLOV7，YOLOV7，YOLOV 7它旨在改善遮挡问题，即从无人机使用深度学习算法捕获的巨大数据集中检测隐藏的牛，以进行准确的牛检测。实验结果表明，与其他两种算法相比，Yolov7的精度为0.612。事实证明，提出的方法优于通常的牛面检测算法，尤其是在非常困难的情况下。
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.
