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UAV thermal imagery dataset and models aid unexploded ordnance screening

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new dataset and object detection models for screening unexploded ordnance (UXO) using UAV thermal imagery. The dataset, collected across four field campaigns in various environmental conditions, contains over 5,800 labeled thermal image pairs. Object detection algorithms like YOLOV11l and RT-DETR-R50 were trained and evaluated to create an automated candidate detection model. The study provides practical recommendations for humanitarian mine action, emphasizing the importance of collecting thermal and RGB imagery together, using representative local data for model calibration, and retaining human review for final assessment. AI

IMPACT This research could improve the efficiency and safety of demining operations through automated detection of unexploded ordnance.

RANK_REASON The cluster contains an academic paper detailing a new dataset and methodology for object detection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

UAV thermal imagery dataset and models aid unexploded ordnance screening

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The cluster contains an academic paper detailing a new dataset and methodology for object detection. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Chad Melton, PhD., Annabelle Kelton ·

    UAV Thermal Imagery for Inert Ordnance Screening: Multi Campaign Dataset Development,Object Detection, and Practical Recommendations

    arXiv:2609.01738v1 Announce Type: new Abstract: Unexploded ordnance (UXO) continues to restrict civilian access, agricultural activity, infrastructure recovery, and environmental remediation in contaminated areas around the world. This study created a multi campaign UAV thermal i…

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