{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T19:14:25Z","timestamp":1773774865769,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:00:00Z","timestamp":1773705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003626","name":"Korea government","doi-asserted-by":"publisher","award":["20-105-E00-005"],"award-info":[{"award-number":["20-105-E00-005"]}],"id":[{"id":"10.13039\/501100003626","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s26061895","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T16:20:14Z","timestamp":1773764414000},"page":"1895","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Driven Adaptive Camouflage Pattern Generation for Helicopter Detection Evasion in Aerial Sensor Imagery Using Fine-Tuned YOLOv8 and Stable Diffusion"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7165-8761","authenticated-orcid":false,"given":"Jonghyeok","family":"Im","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1980-3914","authenticated-orcid":false,"given":"Yeonhong","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1484-3239","authenticated-orcid":false,"given":"Heoung-Jae","family":"Chun","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6317-6308","authenticated-orcid":false,"given":"Kyoungsik","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Den Hollander, R., Adhikari, A., Tolios, I., van Bekkum, M., Bal, A., Hendriks, S., Kruithof, M., Gross, D., Jansen, N., and Perez, G. 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