Dim Target Detection in Infrared Images Using Saliency Algorithms

Abstract

Infrared (IR) target detection and tracking are commonly used in modern defense systems. Target detection is the first and very important step for several surveillance applications. Long distance between imager and targets or bad weather conditions mostly cause dim target appearance with low signal-to-noise ratio (SNR) in IR images. In this study, dim targets in IR images are enhanced and detected using saliency detection algorithms, which have not been used in IR wavelength before. Performances of the algorithms are evaluated on common IR datasets. Algorithms are compared in terms of SNR, receiver operating characteristic (ROC) and area under curve (AUC) score. Effects of parameter selection are also considered for automatic target detection. Furthermore, feasibility of the methods for real-time applications are discussed.