AI-Driven Adaptive Camouflage Pattern Generation for Helicopter Detection Evasion in Aerial Sensor Imagery Using Fine-Tuned YOLOv8 and Stable Diffusion

In aerial sensor systems, detecting helicopters against diverse backgrounds remains challenging due to environmental camouflage. This paper proposes an end-to-end framework for generating adaptive camouflage patterns to evade YOLO-based object detection. Starting with synthetic sensor imagery (background + transparent helicopter overlay), we employ a fine-tuned YOLOv8m for precise VTOL mask extraction, followed by KMeans clustering with Gaussian blur for dominant color extraction from the background. These colors guide Stable Diffusion inpainting to synthesize full-screen camouflage textures, which are then masked and overlapped onto the helicopter region. Evaluated on a 920-image dataset across multiple backgrounds, our method achieves a 97.6% reduction in mAP@0.5 (from 0.8175 to 0.0196) on 751 camouflaged images against a fine-tuned YOLOv8m model, with recall dropping by 95.9%. Even against a helicopter-specialized Defence model, mAP@0.5 drops by 89.6% (from 0.1178 to 0.0123). Ablation studies confirm the synergy of YOLO masking and color-guided inpainting. This sensor-fusion approach enhances stealth in unmanned aerial surveillance, with implications for civilian aviation safety.