TARGET DETECTION IN CLUTTERED ENVIRONMENTS USING INFRA-RED IMAGES

The detection of targets in infra-red imagery is a challenging problem which involves locating small targets in heavily cluttered environments 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. We show that for target detection, it is advantageous to analytically derive the first layer of a CNN to maximize the TCR metric, and then train the rest of the network to optimize the same cost function. We evaluate the performance of the resulting network using a public domain MWIR data set released by the US Army’s Night Vision Laboratories, and compare it to the state-of-the-art detectors such as Faster RCNN and Yolo-v3. Referred to as the TCRNet, the proposed network 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 2 when compared to these leading methods. Ablation studies also show that the proposed approach and metric are superior to learning the entire network from scratch, or using conventional regression metrics such as the mean square error (MSE).