Version 1
: Received: 16 October 2019 / Approved: 17 October 2019 / Online: 17 October 2019 (12:29:29 CEST)
How to cite:
Ammar, A.; Koubaa, A.; Ahmed, M.; Saad, A. Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3. Preprints2019, 2019100195. https://doi.org/10.20944/preprints201910.0195.v1
Ammar, A.; Koubaa, A.; Ahmed, M.; Saad, A. Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3. Preprints 2019, 2019100195. https://doi.org/10.20944/preprints201910.0195.v1
Ammar, A.; Koubaa, A.; Ahmed, M.; Saad, A. Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3. Preprints2019, 2019100195. https://doi.org/10.20944/preprints201910.0195.v1
APA Style
Ammar, A., Koubaa, A., Ahmed, M., & Saad, A. (2019). Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3. Preprints. https://doi.org/10.20944/preprints201910.0195.v1
Chicago/Turabian Style
Ammar, A., Mohanned Ahmed and Abdulrahman Saad. 2019 "Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3" Preprints. https://doi.org/10.20944/preprints201910.0195.v1
Abstract
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.
Keywords
car detection; convolutional neural networks; deep learning; you only look once (yolo); faster r-cnn; unmanned aerial vehicles
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
