# Assessing thermal imagery integration into object detection methods on ground-based and air-based collection platforms

arXiv: https://arxiv.org/abs/2212.12616 · HTML: https://mlsanity.com/paper/2212.12616/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 97 references that are in the map.
- First posted on arXiv 23 Dec 2022.

## Builds on (most cited first, 11 of 11)

- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [PST900: RGB-Thermal Calibration, Dataset and Segmentation Network](https://mlsanity.com/paper/1909.10980/) (2019)
- [FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation](https://mlsanity.com/paper/2110.08988/) (2021)
- [Edge-aware Guidance Fusion Network for RGB Thermal Scene Parsing](https://mlsanity.com/paper/2112.05144/) (2021)
- [Multi-interactive Dual-decoder for RGB-thermal Salient Object Detection](https://mlsanity.com/paper/2005.02315/) (2020)
- [Unsupervised Image-generation Enhanced Adaptation for Object Detection in Thermal images](https://mlsanity.com/paper/2002.06770/) (2020)
- [Enhancing Object Detection in Adverse Conditions using Thermal Imaging](https://mlsanity.com/paper/1909.13551/) (2019)
- [Drone Object Detection Using RGB/IR Fusion](https://mlsanity.com/paper/2201.03786/) (2022)
- [Augmentation of Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models](https://mlsanity.com/paper/2204.08745/) (2022)
- [Predicting cell phone adoption metrics using satellite imagery](https://mlsanity.com/paper/2006.07311/) (2020)
- [The State-of-the-Art Survey on Optimization Methods for Cyber-physical Networks](https://mlsanity.com/paper/2210.03642/) (2022)

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

- [Comparing Surface Landmine Object Detection Models on a New Drone Flyby Dataset](https://mlsanity.com/paper/2410.19807/) (2024)

## Abstract (arXiv)

Object detection models commonly deployed on uncrewed aerial systems (UAS) focus on identifying objects in the visible spectrum using Red-Green-Blue (RGB) imagery. However, there is growing interest in fusing RGB with thermal long wave infrared (LWIR) images to increase the performance of object detection machine learning (ML) models. Currently LWIR ML models have received less research attention, especially for both ground- and air-based platforms, leading to a lack of baseline performance metrics evaluating LWIR, RGB and LWIR-RGB fused object detection models. Therefore, this research contributes such quantitative metrics to the literature. The results found that the ground-based blended RGB-LWIR model exhibited superior performance compared to the RGB or LWIR approaches, achieving a mAP of 98.4%. Additionally, the blended RGB-LWIR model was also the only object detection model to work in both day and night conditions, providing superior operational capabilities. This research additionally contributes a novel labelled training dataset of 12,600 images for RGB, LWIR, and RGB-LWIR fused imagery, collected from ground-based and air-based platforms, enabling further multispectral machine-driven object detection research.