# Enhancing Object Detection in Adverse Conditions using Thermal Imaging

arXiv: https://arxiv.org/abs/1909.13551 · HTML: https://mlsanity.com/paper/1909.13551/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [SSTN: Self-Supervised Domain Adaptation Thermal Object Detection for Autonomous Driving](https://mlsanity.com/paper/2103.03150/) (2021)
- [A Multispectral Automated Transfer Technique (MATT) for machine-driven image labeling utilizing the Segment Anything Model (SAM)](https://mlsanity.com/paper/2402.11413/) (2024)
- [Assessing thermal imagery integration into object detection methods on ground-based and air-based collection platforms](https://mlsanity.com/paper/2212.12616/) (2022)
- [Does Thermal data make the detection systems more reliable?](https://mlsanity.com/paper/2111.05191/) (2021)
- [PDIWS: Thermal Imaging Dataset for Person Detection in Intrusion Warning Systems](https://mlsanity.com/paper/2302.13293/) (2023)

## Abstract (arXiv)

Autonomous driving relies on deriving understanding of objects and scenes through images. These images are often captured by sensors in the visible spectrum. For improved detection capabilities we propose the use of thermal sensors to augment the vision capabilities of an autonomous vehicle. In this paper, we present our investigations on the fusion of visible and thermal spectrum images using a publicly available dataset, and use it to analyze the performance of object recognition on other known driving datasets. We present an comparison of object detection in night time imagery and qualitatively demonstrate that thermal images significantly improve detection accuracy.