Comparison of Object Detection Algorithms for Livestock Monitoring of Sheep in UAV images

This paper presents the EU funded project SPADE, a European initiative that aims to create an Intelligent Ecosystem
utilizing unmanned aerial vehicles (UAVs) for delivering sustainable digital services to various end users in sectors like agriculture, forestry, and livestock. The project’s main goal is to cater to multiple purposes and benefit a wide range of stakeholders. In this paper we specifically concentrate on the livestock use-case and explore how state-of-the-art computer vision algorithms for object detection, tracking, and landscape classification, deployed on edge devices in drones, can offer researchers, conservationists, and farmers a non-intrusive, cost-effective, and efficient method for monitoring livestock increasing animal welfare, and optimize livestock management. We present initial findings by comparing the performance of different state-of-the-art object detectors on publicly available UAV images of sheep. The key performance metrics used are average precision, mean average precision and mean average recall. These findings should enable a better pre-selection of potential object detectors for the presented edge device use case.