# Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3

arXiv: https://arxiv.org/abs/1812.10968 · HTML: https://mlsanity.com/paper/1812.10968/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2018.

## Built on by (most cited first, 4 of 4)

- [RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers](https://mlsanity.com/paper/2207.08857/) (2022)
- [YOLO-based Object Detection in Industry 4.0 Fischertechnik Model Environment](https://mlsanity.com/paper/2301.12827/) (2023)
- [Bounding Box-Free Instance Segmentation Using Semi-Supervised Learning for Generating a City-Scale Vehicle Dataset](https://mlsanity.com/paper/2111.12122/) (2021)
- [Active Data Acquisition in Autonomous Driving Simulation](https://mlsanity.com/paper/2306.13923/) (2023)

## Abstract (arXiv)

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.