Infrared dim target detection method inspired by human vision system

Abstract

Infrared dim target detection has long been a key technology for various systems, such as infrared search and track (IRST) systems and the Space-Based Infrared System (SBIRS). However, it is difficult for traditional detection methods to adapt to different types of complex backgrounds. Therefore, this paper proposes an adaptive infrared dim target detection method based on human visual contrast, motion, prediction, and other characteristics. First, according to the characteristics of different types of background images, the classification preprocessing strategy is adopted to remove noise, suppress the background, and improve the target signal-to-noise ratio. Second, on the basis of the visual contrast and scale adaptation mechanism, we propose an adaptive multi-scale local contrast method to extract the saliency region, and we then analyze the spectral scale to further suppress the background, enhance the target central area, and construct a suspected target set. Finally, the candidate moving target set is obtained by motion region matching using the optical flow method, and a multi-frame screening strategy combined with dynamic pipeline filtering is proposed to identify the target and reduce the false positive rate. Our experiment results indicate that the proposed method can adapt to changes in the target scale and achieve stable and adaptive detection of dim targets in the background of sky, sea-sky, and ground objects.