VSAI: visible signature reduction with camouflage patterns via generative AI algorithm

• Published in: Proceedings of SPIE, the international society for optical engineering Vol. 13199; pp. 1319904 - 1319904-10
• Main Authors: Gulrez, Tauseef, Culpepper, Joanne B., Phung, Son Lam, Le, Hoang Thanh
• Format: Conference Proceeding
• Language: English
• Published: SPIE 01.11.2024
• ISBN: 9781510681064; 151068106X
• ISSN: 0277-786X
• DOI: 10.1117/12.3034275

Hiding platforms in plain sight requires camouflage schemes that blend well with the environment. Such a camouflage scheme needs to cater for different geographical locations, seasons, and times of day. Inspired from nature’s biology, this paper presents a new algorithm, called Visible Signatures AI-generator (VSAI), for generating camouflage patterns iteratively to reduce visible signatures of objects. The proposed algorithm accepts a set of images from any dynamically changing environment. It then generates a customized set of camouflage patterns with colors and textures that are optimized for the environment. We present a novel Generative Adversarial Network (GAN), in which a generator with meta-parameters is iteratively trained to produce camouflage patterns. Simultaneously, a discriminator is trained to differentiate images with or without the embedded camouflage patterns. Unlike the existing methods, the meta-parameters used by our generator are intuitive, explainable, and extendable by the end-users. The experimental results show that the camouflage patterns designed by VSAI are consistent in color, texture, and semantic contents. Furthermore, VSAI produces improved outputs compared to several optical camouflage generation methods, including the Netherland Fractal Patterns, CamoGAN and CamoGen. The full end-to-end pattern generation process can operate at a speed of 1.21 second per pattern. Evaluated on the benchmark dataset Cityscapes, the YOLOv8 detector shows a significantly reduced target detection performance when our camouflage patterns are applied, yielding an mAP@0.5 detection score of 7.2% and an mAP@0.5:0.95 detection score of 3.2%. Compared to CamoGAN, our camouflage generation method leads to an average reduction of 4.0% in the mAP@0.5:0.95 detection score.