Small Target Detection Utilizing Robust Methods of the Human Visual System for IRST

Abstract

Robust detection of small targets is very important in IRST (Infrared Search and Track). This paper presents a novel mathematical method for the incoming target detection problem in cluttered background motivated from the robust properties of human visual system (HVS). The HVS shows the best efficiency and robustness for an object detection task. The robust properties of the HVS are contrast mechanism, multi-resolution representation, size adaptation, and pop-out phenomena. Based on these facts, a plausible computational model integrating these facts is proposed using Laplacian scale-space theory and Tune-Max based optimization method. Simultaneous target signal enhancement and background clutter suppression is achieved by tuning and maximizing the signal-to-clutter ratio (TMSCR) in Laplacian scale-space. At the first stage, the Tune-Max of the signal to background contrast produces candidate targets with adapted scale. At the second stage, the Tune-Max of the signal-to-clutter ratio (SCR) produces maximal SCR which is used to popout detections. Experimental evaluation results for the incoming target sequence validate the upgraded detection capability of the proposed method compared with the Top-hat method at the same false alarm rate. Experimental results for the six kinds of cluttered background images show that the proposed TMSCR produces less false alarms (4.3 times reduction) compared to the Top-hat at the same detection rate.

Small target detection using edge-preserving background estimation based on maximum patch similarity

“…[17][18][19] A traditional LCM for small infrared target detection was proposed 3 based on HSV. However, the region with high brightness PSEN which usually exists in the infrared image also have contrast differences with its surrounding areas; therefore, the LCM may leads to a high false alarm and a low signal-to-clutter ratio (SCR) 20 of the image.…”

Rectilinear-motion space inversion-based detection approach for infrared dim air targets with variable velocities

“…3 A small target in IR images is often characterized by low contrast and low SNR. The size of a dim target in the imaging plane of IR detectors is only several pixels with regularity.…”

“…Usually, a parametric target model of point-spread function is used to represent the target in imaging plane. The target model is defined as 1,3,4,15 E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 1 ; 3 2 6 ; 5 7 5 sðn x ; n y jn xc ; n yc ; τ x ; τ y Þ…”

“…Recently, many researchers have made further efforts to improve algorithm performance. Meanwhile, they have proposed several algorithms, such as multi-scale new white top-hat transformation (MNWTH) algorithm based on new top-hat transformation, 2 Laplacian of Gaussian filter, 3,4 and local contrast measure (LCM) algorithm 5,6 based on contrast mechanism. Commonly, those single-frame detection algorithms try to detect the target spatially.…”

A simple and efficient saliency extraction method based on multi-scale horizon-directional filter for infrared dim small target detection

“…These properties make small targets highly difficult to be detected. Many algorithms have been proposed to detect infrared small targets, including mathematical morphology based algorithms [9][10][11][12][13][14], filter-based algorithms [6][7][8], wavelet based algorithm [15], machine learning based algorithms [16][17][18] and saliency based algorithms [19][20][21].…”