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ESTD Year: 2017 | Impact Factor (2026): 8.7
DOI Prefix: 10.47001/IRJIET
ESTD Year: 2017 | Impact Factor (2026): 8.7
DOI Prefix: 10.47001/IRJIET
Vol 10 No 6 (2026): Volume 10, Issue 6, June 2026 | Pages: 214-226
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 24-06-2026
Adaptive camouflage is an emerging technology that enables military vehicles to dynamically modify their visual appearance according to changing environmental conditions, thereby reducing detectability and improving survivability in diverse operational environments. Conventional camouflage techniques rely on fixed colour patterns that often become ineffective across varying terrains. This paper presents a low-cost Adaptive Camouflage System for military vehicles that integrates computer vision, deep learning, image processing, and environmental sensing to achieve real-time adaptation. The system uses a Raspberry Pi 3 Model B+ and Raspberry Pi Cam V1.3 to capture environmental images. A Convolutional Neural Network (CNN)-based terrain classification model identifies terrain types such as forest, desert, mountain, beach, and wetland. OpenCV-based image processing is applied to extract dominant colours and visual features from the scene. Environmental parameters including light intensity, temperature, and humidity are measured using BH1750 and DHT11 sensors to provide additional contextual information. Based on terrain classification, extracted features, and sensor data, appropriate camouflage patterns are generated and displayed on a TFT display. The system is evaluated under multiple environmental conditions to assess classification accuracy, adaptability, and real-time performance. Experimental results show that the proposed framework effectively identifies terrain types, extracts relevant environmental features, and generates suitable camouflage outputs in real time with low computational complexity and cost. The findings demonstrate that combining deep learning-based terrain recognition with image processing improves environmental awareness and enhances camouflage adaptation compared to colour-only approaches. The proposed system provides a foundation for intelligent camouflage solutions for next-generation military vehicles and autonomous defence platforms.
Adaptive Camouflage, Terrain Classification, Convolutional Neural Network, Raspberry Pi, OpenCV, Computer Vision, Military Vehicles, Environmental Sensing, Image Processing.
Shruti Adsul, Akanksha Agre, & Divyanshu Raj. (2026). CamoGen: Next Generation Camouflage Powered by AI. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(6), 214-226. Article DOI https://doi.org/10.47001/IRJIET/2026.106028
This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
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