无人机影像目标自动提取在羊群围捕中的应用研究

Automated Sheep Detection from UAV Images for the Application of Sheep Roundup

挪威每年约有210万只羊在森林和山区自由放牧,在放牧季节结束时,农民们必须找到并围拢他们的羊,而这个过程非常耗时费力。现有跟踪羊群的方法包括铃铛、无线电铃、电子耳标和无人机等,这些方法受限于单价、信号覆盖、自动化程度低等缺点,无法高效自动地找到野外的羊。本文提出了一种基于无人机图像的羊群自动检测算法,使用ResNet和ResNeXt作为主干网络,设计了一种针对RGB和红外图像的羊群自动检测模型网络结构。本文分别利用RGB、红外和二者融合图像作为输入数据,对比模型检测效果,同时探讨了不同模型复杂性,并验证了模型泛化能力。实验结果表明,使用融合数据比单独使用一种数据能够获得更好的平均精度结果。不同复杂度和不同输入数据类型,其模型的验证数据集平均精度在69.6%~96.3%之间,处理时间在0.1~0.6 s之间。在置信阈值为0.5时,最优模型取得了97.9%的网格准确率和90.1%的召回率,这对应于在验证数据集中检测到97.5%的羊,因此,本文提出的基于无人机RGB和红外图像的羊群自动检测算法可以为改进羊群围捕做出一定贡献。

Each year,approximately 2.1 million sheep are released to graze freely in vast,forest-covered,and mountainous areas throughout Norway.At the end of the grazing season,farmers must find and round up their sheep.This can be a time consuming and challenging process because of the large area and cluttered nature of the sheep grazing environment.Existing technologies that help farmers find their sheep,such as bells,radio bells,electronic ear tags and UAVs,are limited by the cost,signal coverage,and low degree of automation,which cannot efficiently and automatically locate sheep in the wild.This study proposes an automatic sheep detection algorithm using UAV images.A model architecture using the ResNet and ResNeXt as the backbone networks is designed to address the automatic sheep detection task from UAV RGB and infrared images.Our study evaluates how well this model meets performance and processing speed requirements of a real-world application.We also compare models using fused RGB and infrared data to models using either RGB or infrared as input,and further explore the model complexity and generalization ability.Results show that fusion of RGB and infrared data yields better average precision results than using single RGB or infrared dataset in the model.The set of optimal solutions achieve average precision scores in the range of 69.6%to 96.3%with inference times ranging from 0.1 to 0.6 seconds per image.The most accurate network achieves a grid precision of 97.9%and a recall of90.1%,at a confidence threshold of 0.5.This corresponds to the detection of 97.5%of the sheep in the validation dataset.These satisfactory results demonstrate the great potential of the proposed automatic sheep detection method using multi-channel UAV images for improving sheep roundup.