Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3
Ref: CISTER-TR-190620 Publication Date: 5 to 7, Feb, 2019
Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3
Ref: CISTER-TR-190620 Publication Date: 5 to 7, Feb, 2019Abstract:
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.
Events:
Document:
1st International Conference on Unmanned Vehicle Systems (UVS 2019).
Muscat, Oman.
DOI:10.1109/UVS.2019.8658300.
ISBN: 978-1-5386-9368-1.
Record Date: 14, Jun, 2019