Simulation Learning Method for Discovery of Camouflage Targets Based on Deep Neural Networks

Aiming at the problem of serious lack of effective samples in the automatic discovery of camouflage targets, a simulation training method is proposed based on the sample simulation of a deep neural network and the technical idea of AlphaGo. A simulation synthesis model of camouflage scenes is established. The compound algorithm in the image space, the deep feature extraction strategy of scene images, the measurement strategy of target fusion degree, and the sampling algorithm for graph clustering are designed, respectively. Thus the representative samples for camouflage scene simulation are batch generated, which can be used for the deep neural network training and learning. Moreover, a discovery model of camouflage targets is designed based on a deep residual neural network, in which a multi-scale network training strategy is considered. The experimental results on the simulated samples and real scene images show that the proposed method can be effectively used for the automatic discovery and evaluation of camouflage targets.