Infrared Small Dim Target Detection Under Maritime Near Sea–Sky Line Based on Regional-Division Local Contrast Measure

Abstract

Infrared (IR) small dim target detection near the sea-sky line (SSL) is crucial for enhancing the early warning capability of maritime vehicles. However, the interferences caused by the strong contrast have not been properly addressed. Consequently, a specially designed algorithm Regional-Division Local Contrast Measure (RDLCM) that focuses on the detection of infrared small dim targets appearing near the SSL is proposed. First, an SSL detection module based on a lightweight Convolutional Neural Network (CNN) is devised to achieve fast pixel-level SSL detection. Then, a set of regional-division windows (RDW) are designed according to the strong grayscale contrast distribution around the SSL, through the division of the effective regions, the RDWs could realize the potential extraction and refinement of the IR small dim targets that appear near the SSL. Experiments on three IR image sequences demonstrate that the proposed algorithm achieves the best detection accuracy among the classical and the state-of-the-art algorithms in comparison, and runs at 44 frames per second (FPS) which could meet real-time requirements. The code and dataset are available at RDLCM.