  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. A
total of 39 training experiments were conducted to account for the effect of
different hyperparameter values. The objective of this work is to conduct the
most robust and exhaustive comparison between these two cutting-edge algorithms
on the specific domain of aerial images. By using a variety of metrics, we show
that YOLOv3 yields better performance in most configurations, except that it
exhibits a lower recall and less confident detections when object sizes and
scales in the testing dataset differ largely from those in the training
dataset.
