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Article

Vehicle Detection from Aerial Images Using Deep Learning: A Comparative Study

1
Department of Computer Science, College of Computer & Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
2
CISTER Research Centre, ISEP, Polytechnic Institute of Porto, 4200-465 Porto, Portugal
3
SEICT Lab, LR18ES44, Enicarthage, University of Carthage, Tunis 1054, Tunisia
*
Authors to whom correspondence should be addressed.
Academic Editor: Giovanni Dimauro
Electronics 2021, 10(7), 820; https://doi.org/10.3390/electronics10070820
Received: 21 February 2021 / Revised: 18 March 2021 / Accepted: 19 March 2021 / Published: 30 March 2021
This paper addresses the problem of car detection from aerial images using Convolutional Neural Networks (CNNs). This problem presents additional challenges as compared to car (or any object) detection from ground images because the features of vehicles from aerial images are more difficult to discern. To investigate this issue, we assess the performance of three state-of-the-art CNN algorithms, namely Faster R-CNN, which is the most popular region-based algorithm, as well as YOLOv3 and YOLOv4, which are known to be the fastest detection algorithms. We analyze two datasets with different characteristics to check the impact of various factors, such as the UAV’s (unmanned aerial vehicle) altitude, camera resolution, and object size. A total of 52 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 three cutting-edge algorithms on the specific domain of aerial images. By using a variety of metrics, we show that the difference between YOLOv4 and YOLOv3 on the two datasets is statistically insignificant in terms of Average Precision (AP) (contrary to what was obtained on the COCO dataset). However, both of them yield markedly better performance than Faster R-CNN in most configurations. The only exception is that both of them exhibit a lower recall when object sizes and scales in the testing dataset differ largely from those in the training dataset. View Full-Text
Keywords: car detection; convolutional neural networks; deep learning; Faster R-CNN; unmanned aerial vehicles; YOLOv3; YOLOv4 car detection; convolutional neural networks; deep learning; Faster R-CNN; unmanned aerial vehicles; YOLOv3; YOLOv4
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MDPI and ACS Style

Ammar, A.; Koubaa, A.; Ahmed, M.; Saad, A.; Benjdira, B. Vehicle Detection from Aerial Images Using Deep Learning: A Comparative Study. Electronics 2021, 10, 820. https://doi.org/10.3390/electronics10070820

AMA Style

Ammar A, Koubaa A, Ahmed M, Saad A, Benjdira B. Vehicle Detection from Aerial Images Using Deep Learning: A Comparative Study. Electronics. 2021; 10(7):820. https://doi.org/10.3390/electronics10070820

Chicago/Turabian Style

Ammar, Adel, Anis Koubaa, Mohanned Ahmed, Abdulrahman Saad, and Bilel Benjdira. 2021. "Vehicle Detection from Aerial Images Using Deep Learning: A Comparative Study" Electronics 10, no. 7: 820. https://doi.org/10.3390/electronics10070820

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