Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3
Ref: CISTER-TR-200107 Publication Date: 2020
Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3
Ref: CISTER-TR-200107 Publication Date: 2020Abstract:
In this paper, we address the problem of car detection from aerial images using Convolutional Neural Networks
(CNN). This problem presents additional challenges as compared
to car (or any object) detection from ground images because
features of vehicles from aerial images are more difficult to
discern. To investigate this issue, we assess the performance of two
state-of-the-art CNN algorithms, namely Faster R-CNN, which is
the most popular region-based algorithm, and YOLOv3, which
is known to be the fastest detection algorithm. We analyze two
datasets with different characteristics to check the impact of
various factors, such as UAV’s altitude, camera resolution, and
object size. The objective of this work is to conduct a robust
comparison between these two cutting-edge algorithms. By using
a variety of metrics, we show that none of the two algorithms
outperforms the other in all cases.
Adel Ammar
Anis KoubâaDocument:
View
DownloadRecord Date: 14, Jan, 2020