Detecting and Counting Sheep with a Convolutional Neural Network

Abstract

Counting livestock is generally done only during major events, such as drenching, shearing or loading, and thus farmers get stock numbers sporadically throughout the year. More accurate and timely stock information would enable farmers to manage their herds better. Additionally, prompt response to any stock in distress is extremely valuable, both in terms of animal welfare and the avoidance of financial loss. In this regard, the evolution of deep learning algorithms and Unmanned Aerial Vehicles (UAVs) is forging a new research area for remote monitoring and counting of different animal species under various climatic conditions. In this paper, we focus on detecting and counting sheep in a paddock from UAV video. Sheep are counted using a model based on Region-based Convolutional Neural Networks and the results are then compared with other techniques to evaluate their performance.