Scale invariant small target detection by optimizing signal-to-clutter ratio in heterogeneous background for infrared search and track

摘要

This paper presents a novel mathematical method for incoming target detection in a cluttered background motivated by the robust properties of the human visual system (HVS). The robust detection of small targets is very important in IRST (Infrared Search and Track) applications for self-defense or attacks. HVS shows the best efficiency and robustness for the task of object detection in cluttered backgrounds. The robust properties of HVS include the contrast mechanism of figure-ground, multi-resolution representation of an object, size adaptation of object boundary, and pop-out phenomena in a complex environment. Based on these facts, a plausible computational model integrating these facts is proposed using Laplacian scale-space theory and an optimization method. Simultaneous target signal enhancement and background clutter suppression are achieved by tuning and maximizing the signal-to-clutter ratio (TM-SCR) in Laplacian scale-space. At the first stage, Tune–Max of the signal to background contrast produces candidate targets with estimated target scale. At the second stage, Tune–Max of the signal-to-clutter ratio (SCR) produces maximal SCR that is used to sort the detection results. Especially, the row-directional-local background removal filter (RD-LBRF) is preprocessed in the horizontal region to enhance the TM-SCR method. The evaluation results of incoming target sequence validate the detection capability of the proposed method from dim, small targets to strong, large targets in comparison with the Top-hat method at the same rate of false alarms. The experimental results of various cluttered background images show that the proposed TM-SCR produces less false alarms (4.3 times reduction) compared to that of the Top-hat at the same detection rate. Finally, TM-SCR after RD-LBRF can maximize the detection rate in horizontal regions.