Infrared Variation Optimized Deep Convolutional Neural Network for Robust Automatic Ground Target Recognition

Abstract

Automatic infrared target recognition (ATR) is a traditionally unsolved problem in military applications because of the wide range of infrared (IR) image variations and limited number of training images, which is caused by various 3D target poses, non-cooperative weather conditions, and difficult target acquisition environments. Recently, deep convolutional neural network-based approaches in RGB images (RGB-CNN) showed breakthrough performance in computer vision problems, such as object detection and classification. The direct use of the RGB-CNN to IR ATR problem fails to work because of the IR database problems. This paper presents a novel infrared variation-optimized deep convolutional neural network (IVO-CNN) by considering database management, such as increasing the database by a thermal simulator, controlling the image contrast automatically and suppressing the thermal noise to reduce the effects of infrared image variations in deep convolutional neural network-based automatic ground target recognition. The experimental results on the synthesized infrared images generated by the thermal simulator (OKTAL-SE) validated the feasibility of IVO-CNN for military ATR applications.