Maritime Small Target Detection Based on Appearance Stability and Depth-Normalized Motion Saliency in Infrared Video With Dense Sunglints

Abstract

Maritime infrared (IR) small target detection in dense sunglint environments has always been challenging. It is difficult for existing methods to distinguish the small target from dense and strong sea clutter because the sea clutter caused by the sunglints has similar spatial-temporal characteristics to small targets. This paper proposes a method based on appearance stability and depth-normalized motion saliency (AS-DNMS) for small target detection in IR videos or sequences. First, on each frame of the IR video, the isotropic salience measure (ISM) based on principal curvature filtering is proposed for target enhancement, and an adaptive threshold is used to extract a stable number of candidate targets. Then, we use improved pipeline filtering to form trajectory chains of the candidate targets extracted from consecutive frames. To improve the matching accuracy of pipeline filtering, we propose an inverse optical flow method to predict the local images of the candidate targets. Third, the motion vectors of the candidate targets are extracted and depth-normalized. According to the principle of projection perspective, a depth normalization model based on the position of the sea-sky line is established, providing a simple and low-cost solution for acquiring depth information of maritime targets under sea-sky backgrounds. Finally, the appearance stability measure (ASM) and the depth-normalized motion saliency measure (DNMSM) are calculated to construct the AS-DNMS joint feature. A double asymptote decision function is employed to determine the real target. The experimental results show that our method has better detection performance in dense sunglint environments than the baseline methods.