A survey on deep learning-based object detectionalgorithms for drones

Type : Article de conférence
Auteur(s) :  BOUGUETTAYA Abdelmalek, Kechida Ahmed, TABERKIT Mohammed Amine
Année :  2019
Domaine : Electronique
Conférence: 4th International Conference on Embedded Systems in Telecommunications and Instrumentation
Résumé en PDF :  (résumé en pdf)
Fulltext en PDF :  (.pdf)
Mots clés :  Computer vision, UAV, Deep Learning, Object Detection, Convolutional Neural Network

Résumé : 

Unmanned Aerial Vehicles (UAVs) are being used in a very large number of applications. Building an intelligent UAVis a very exciting and challenging topic. Recently, deep learning and computer vision are highly used for the purpose of realizing a fully-autonomous drone that does not need human intervention. Computer vision is a field focused on enabling drones to interpret and understand the content of an image or a video using Convolutional Neural Networks (CNNs). This paper focuses on reviewing recent deep learning-based object detection algorithms used for UAVs. We will discuss the most important research papers and techniques which helped improve the object detection state-of-the-art for drones. Finally, we will conclude this reviewing with a description of the main challenges for the application of deep learning for drone-based solutions.