Car Detection using Unmanned Aerial Vehicles
Comparison between Faster R-CNN and YOLOv3
Presented in: UVS-Oman 2019, The 1st Unmanned Vehicle Systems conference in Oman, 6 Feb 2019
Location: Prince Sultan University, Auditorium Men Campus-Building 105 - G-C02 - 2nd Floor
Unmanned Aerial Vehicles are increasingly being used in surveillance and traffic monitoring thanks to their high mobility and ability to cover areas at different altitudes and locations. One of the major challenges is to use aerial images to accurately detect cars and count-them in real-time for traffic monitoring purposes. Several deep learning techniques were recently proposed based on convolution neural network (CNN) for real-time classification and recognition in computer vision. However, their performance depends on the scenarios where they are used. In this paper, we investigate the performance of two state-of-the art CNN algorithms, namely Faster R-CNN and YOLOv3, in the context of car detection from aerial images. We trained and tested these two models on a large car dataset taken from UAVs. We demonstrated in this paper that YOLOv3 outperforms Faster R-CNN in sensitivity and processing time, although they are comparable in the precision metric.
How to Cite
Bilel Benjdira, Taha Khursheed, Anis Koubaa, Adel Ammar, Kais Ouni,
Car Detection using Unmanned Aerial Vehicles: Comparison betweenFaster R-CNN and YOLOv3
in the First Unmanned Vehicle Systems Conference in Oman, IEEE, Feb 2019.