Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3
Open accessAbstract
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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
For the original and image patch layers of the image spatial pyramid, an integrated decision-making algorithm was formulated to reduce multiple detections of same objects.
“For the original and image patch layers of the image spatial pyramid, an integrated decision-making algorithm was formulated to reduce multiple detections of same objects.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [72]
[72] proposed ISPDM with YOLOv3 framework to improve vehicle detection performance in high resolution multi-scale aerial images.
“In addition, to improve vehicle detection performance in high resolution multi-scale aerial images, [72] proposed Image Spatial Pyramid Detection Model (ISPDM) with YOLOv3 framework.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [72]
The YOLOv3 with ISPDM framework achieves an AP of 68.78% on a custom dataset.
“[72] | YOLOv3 ISPDM | with | CNN | Custom | 3840×2160 & 1372× 941 | Different altitudes | -- | One class (Car) | 68.78 | --”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [72]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · see citing works
The Pascal Visual Object Classes Challenge: A Retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139 · see citing works
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A Systematic Review of Drone Based Road Traffic Monitoring System
Bisio, I., Garibotto, C., Haleem, H., Lavagetto, F., & Sciarrone, A. (2022) · IEEE Access · doi:10.1109/access.2022.3207282 · Cited by 121 · see citing works
“Moreover, Benjdira et al. [72] used Faster R-CNN [83] and YOLOv3 [25] to detect cars for traffic monitoring purpose.”
MethodOrientation- and Scale-Invariant Multi-Vehicle Detection and Tracking from Unmanned Aerial Videos
Wang, J., Simeonova, S., & Shahbazi, M. (2019) · Remote Sensing · doi:10.3390/rs11182155 · Cited by 50 · see citing works
- Authors
- Benjdira, B., Khursheed, T., Koubaa, A., Ammar, A., & Ouni, K.
- Affiliations
- Prince Sultan University (SA)
- Imam Mohammad ibn Saud Islamic University (SA)
- Venue
- 2019 1st International Conference on Unmanned Vehicle Systems-Oman (UVS)
- Publisher
- IEEE
- Published
- 2019-02
- Pages
- 1-6
- OpenAlex
- W2907312552
- Cited by
- 307
- References
- 6
- Citations by year
- 2026: 7 · 2025: 15 · 2024: 38 · 2023: 50 · 2022: 61 · 2021: 62 · 2020: 57 · 2019: 17
- FWCI
- 16.06
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- International Conference on Unmanned Vehicle Systems-Oman (UVS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Prince Sultan University
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- car detection, unmanned aerial vehicles, Faster R-CNN, YOLOv3, object detection, traffic monitoring, aerial images, real-time detection, vehicle counting
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
