# Comparison of Object Detection Algorithms Using Video and Thermal Images Collected from a UAS Platform: An Application of Drones in Traffic Management

arXiv: https://arxiv.org/abs/2109.13185 · HTML: https://mlsanity.com/paper/2109.13185/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 27 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Moving Objects Detection with a Moving Camera: A Comprehensive Review](https://mlsanity.com/paper/2001.05238/) (2020)
- [On the Role and the Importance of Features for Background Modeling and Foreground Detection](https://mlsanity.com/paper/1611.09099/) (2016)
- [An Efficient Optical Flow Based Motion Detection Method for Non-stationary Scenes](https://mlsanity.com/paper/1811.08290/) (2018)
- [Compressive Sensing Approaches for Autonomous Object Detection in Video Sequences](https://mlsanity.com/paper/1705.00002/) (2017)

## Built on by (most cited first, 1 of 1)

- [DARTS: A Drone-Based AI-Powered Real-Time Traffic Incident Detection System](https://mlsanity.com/paper/2510.26004/) (2025)

## Abstract (arXiv)

There is a rapid growth of applications of Unmanned Aerial Vehicles (UAVs) in traffic management, such as traffic surveillance, monitoring, and incident detection. However, the existing literature lacks solutions to real-time incident detection while addressing privacy issues in practice. This study explored real-time vehicle detection algorithms on both visual and infrared cameras and conducted experiments comparing their performance. Red Green Blue (RGB) videos and thermal images were collected from a UAS platform along highways in the Tampa, Florida, area. Experiments were designed to quantify the performance of a real-time background subtraction-based method in vehicle detection from a stationary camera on hovering UAVs under free-flow conditions. Several parameters were set in the experiments based on the geometry of the drone and sensor relative to the roadway. The results show that a background subtraction-based method can achieve good detection performance on RGB images (F1 scores around 0.9 for most cases), and a more varied performance is seen on thermal images with different azimuth angles. The results of these experiments will help inform the development of protocols, standards, and guidance for the use of drones to detect highway congestion and provide input for the development of incident detection algorithms.