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

• Published in: IEEE geoscience and remote sensing letters Vol. 20; pp. 1 - 5
• Main Authors: Fu, Jian, Li, Fan, Zhao, Jianhui, Tong, Jie, Zhang, He
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Convolutional neural network (CNN); Detection; Frames per second; Geoscience and remote sensing; Gray-scale; infrared (IR) small dim target detection; Infrared imaging; Junctions; local contrast measure (LCM); Neural networks; Object detection; Real-time systems; Sea measurements; sea-sky line (SSL); Target detection; Velocity measurement
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2023.3316272

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 IR 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 (RDWs) are designed according to the strong grayscale contrast distribution around the SSL, and 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 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.