Title:
Detection of Cattle Using Drones and Convolutional Neural Networks.
Detection of Cattle Using Drones and Convolutional Neural Networks.
Authors:
Rivas Camacho, Alberto; Chamoso Santos, Pablo; González Briones, Alfonso; Corchado Rodríguez, Juan M.
Journal:
Sensors. Volume 18 (7), pp. 1-15. MDPI.
Publication date:
July 2018
ISSN:
1424-8220
Multirotor drones have been one of the most important technological advances of the last decade. Their mechanics are simple compared to other types of drones and their possibilities in flight are greater. For example, they can take-off vertically. Their capabilities have therefore brought progress to many professional activities. Moreover, advances in computing and telecommunications have also broadened the range of activities in which drones may be used. Currently, artificial intelligence and information analysis are the main areas of research in the field of computing. The case study presented in this article employed artificial intelligence techniques in the analysis of information captured by drones. More specifically, the camera installed in the drone took images which were later analyzed using Convolutional Neural Networks (CNNs) to identify the objects captured in the images. In this research, a CNN was trained to detect cattle, however the same training process could be followed to develop a CNN for the detection of any other object. This article describes the design of the platform for real-time analysis of information and its performance in the detection of cattle.