Infrared Target Detection in Cluttered Environments by Maximization of a Target to Clutter Ratio (TCR) Metric Using a Convolutional Neural Network

Abstract

Infrared target detection is a challenging computer vision problem which involves detecting small targets in heavily cluttered conditions while maintaining a low false alarm rate. We propose a network that optimizes a “target to clutter ratio”(TCR) metric defined as the ratio of the output energies produced by the network in response to targets and clutter. A TCR-network (TCRNet) is presented in which the filters of the first convolutional layer are composed of the eigenvectors most responsive to targets or to clutter. These vectors are analytically derived via a closed form optimization of the TCR metric. The remaining convolutional layers are trained using a novel cost function also designed to optimize the TCR criterion. We evaluate the performance of the TCRNet using a public domain medium wave infrared dataset released by the US Army's Night Vision Laboratories, and compare it to the state-of-the-art detectors such as Faster regions with convolutional neural networks (R-CNN) and Yolo-v3. The TCRNet demonstrates state-of-the-art results with greater than 30% improvement in probability of detection while reducing the false alarm rate by more than a factor of two when compared to these leading methods. Experimental results are shown for both day and night time images, and ablation studies are presented which demonstrate the contribution of the first layer eigenfilters, additional convolutional layers, and the benefit of the TCR cost function.