CNN-based battlefield classification and camouflage texture generation for real environment

摘要

It is critical to understand the environment in which the military forces are deployed. For self-defence and greater concealment, they should camouflage themselves. Camouflage is being used by the defence system to hide its personnel and equipment. The industry demands an intelligent system that can categorise the battlefield before generating texture for camouflaging their assets and objects, allowing them to adopt the conspicuous features of the scene. In this study, a CNN-based battlefield classification model has been developed to learn background information and classify the terrain. The study also intended to develop the texture for specific terrain by matching its salient features and boosting the effectiveness of the camouflage. Saliency maps have been used to measure the effectiveness of blending a camouflaged object into an environment.