Departamento de Ingenierías Mecánica, Informática y Aeroespacial, Universidad de León, 24071 León, Spain.
Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7522 NB Enschede, The Netherlands.
Sensors (Basel). 2022 Jul 16;22(14):5321. doi: 10.3390/s22145321.
Livestock farming is assisted more and more by technological solutions, such as robots. One of the main problems for shepherds is the control and care of livestock in areas difficult to access where grazing animals are attacked by predators such as the Iberian wolf in the northwest of the Iberian Peninsula. In this paper, we propose a system to automatically generate benchmarks of animal images of different species from iNaturalist API, which is coupled with a vision-based module that allows us to automatically detect predators and distinguish them from other animals. We tested multiple existing object detection models to determine the best one in terms of efficiency and speed, as it is conceived for real-time environments. YOLOv5m achieves the best performance as it can process 64 FPS, achieving an mAP (with IoU of 50%) of 99.49% for a dataset where wolves (predator) or dogs (prey) have to be detected and distinguished. This result meets the requirements of pasture-based livestock farms.
畜牧业越来越多地借助于机器人等技术解决方案来辅助。对于牧羊人来说,主要问题之一是在难以进入的地区控制和照顾牲畜,这些地区的放牧动物会受到伊比利亚狼等捕食者的攻击,伊比利亚狼分布在伊比利亚半岛西北部。在本文中,我们提出了一个系统,该系统可以通过 iNaturalist API 自动生成不同物种的动物图像基准,该系统与基于视觉的模块结合使用,该模块可以自动检测捕食者并将其与其他动物区分开来。我们测试了多个现有的目标检测模型,以确定在效率和速度方面表现最佳的模型,因为它是为实时环境设计的。YOLOv5m 的性能最佳,因为它可以处理 64 FPS,在一个必须检测和区分狼(捕食者)或狗(猎物)的数据集上,mAP(IOU 为 50%)达到 99.49%。该结果满足牧场牲畜农场的要求。