# Multi-temporal Adaptive Red-Green-Blue and Long-Wave Infrared Fusion for You Only Look Once-Based Landmine Detection from Unmanned Aerial Systems

arXiv: https://arxiv.org/abs/2512.20487 · HTML: https://mlsanity.com/paper/2512.20487/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 100 references that are in the map.
- First posted on arXiv 23 Dec 2025.

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

- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series](https://mlsanity.com/paper/2406.19407/) (2024)
- [A Multispectral Automated Transfer Technique (MATT) for machine-driven image labeling utilizing the Segment Anything Model (SAM)](https://mlsanity.com/paper/2402.11413/) (2024)
- [Comparative Analysis of YOLOv9, YOLOv10 and RT-DETR for Real-Time Weed Detection](https://mlsanity.com/paper/2412.13490/) (2024)
- [MineInsight: A Multi-sensor Dataset for Humanitarian Demining Robotics in Off-Road Environments](https://mlsanity.com/paper/2506.04842/) (2025)
- [YOLO-MST: Multiscale deep learning method for infrared small target detection based on super-resolution and YOLO](https://mlsanity.com/paper/2412.19878/) (2024)
- [Under the Sand: Navigation and Localization of a Micro Aerial Vehicle for Landmine Detection with Ground Penetrating Synthetic Aperture Radar](https://mlsanity.com/paper/2106.10108/) (2021)
- [Comparing Surface Landmine Object Detection Models on a New Drone Flyby Dataset](https://mlsanity.com/paper/2410.19807/) (2024)
- [AMLID: An Adaptive Multispectral Landmine Identification Dataset for Drone-Based Detection](https://mlsanity.com/paper/2512.18738/) (2025)

## Abstract (arXiv)

Landmines remain a persistent humanitarian threat, with 110 million actively deployed mines across 60 countries, claiming 26,000 casualties annually. This research evaluates adaptive Red-Green-Blue (RGB) and Long-Wave Infrared (LWIR) fusion for Unmanned Aerial Systems (UAS)-based detection of surface-laid landmines, leveraging the thermal contrast between the ordnance and the surrounding soil to enhance feature extraction. Using You Only Look Once (YOLO) architectures (v8, v10, v11) across 114 test images, generating 35,640 model-condition evaluations, YOLOv11 achieved optimal performance (86.8% mAP), with 10 to 30% thermal fusion at 5 to 10m altitude identified as the optimal detection parameters. A complementary architectural comparison revealed that while RF-DETR achieved the highest accuracy (69.2% mAP), followed by Faster R-CNN (67.6%), YOLOv11 (64.2%), and RetinaNet (50.2%), YOLOv11 trained 17.7 times faster than the transformer-based RF-DETR (41 minutes versus 12 hours), presenting a critical accuracy-efficiency tradeoff for operational deployment. Aggregated multi-temporal training datasets outperformed season-specific approaches by 1.8 to 9.6%, suggesting that models benefit from exposure to diverse thermal conditions. Anti-Tank (AT) mines achieved 61.9% detection accuracy, compared with 19.2% for Anti-Personnel (AP) mines, reflecting both the size differential and thermal-mass differences between these ordnance classes. As this research examined surface-laid mines where thermal contrast is maximized, future research should quantify thermal contrast effects for mines buried at varying depths across heterogeneous soil types.