AI-Driven Adaptive Camouflage Pattern Generation for Helicopter Detection Evasion in Aerial Sensor Imagery Using
Jonghyeok Im1, Yeonhong Kim1, Heoung-Jae Chun1
1School of Mechanical Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea.
Sensors (Basel, Switzerland)
|March 28, 2026
Summary
This study introduces adaptive camouflage for helicopters to evade object detection systems. The method significantly reduces detection accuracy, enhancing stealth for aerial surveillance and improving aviation safety.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Aerospace Engineering
Background:
- Detecting helicopters in aerial surveillance is difficult due to camouflage.
- Existing object detection models struggle with environmental blending.
Purpose of the Study:
- To develop an end-to-end framework for generating adaptive camouflage patterns.
- To evade YOLO-based object detection systems for enhanced aerial stealth.
Main Methods:
- Utilized fine-tuned YOLOv8m for helicopter mask extraction.
- Employed KMeans clustering and Gaussian blur for background color analysis.
- Applied Stable Diffusion inpainting for camouflage texture synthesis and application.
Main Results:
- Achieved a 97.6% reduction in mAP@0.5 against a fine-tuned YOLOv8m model.
- Reduced detection recall by 95.9% on camouflaged images.
- Demonstrated an 89.6% mAP@0.5 reduction against a specialized defense model.
Conclusions:
- The proposed sensor-fusion camouflage framework effectively evades object detection.
- This approach enhances stealth capabilities for unmanned aerial surveillance.
- Findings have potential implications for civilian aviation safety.
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