Small Single Target Detection Algorithm Using a Standard Deviation Filter and Density-Based Clustering in Infrared Image

Small target detection in an infrared search and track (IRST) system is challenging because IR imaging lacks shape information, and a low signal-to-noise ratio. The existing small IR target detection methods have achieved high detection performance without considering the execution time. Hence, we propose a fast and powerful single-frame IR small target detection algorithm with low computational cost, while maintaining excellent detection performance. The IR intensity difference value based on the standard deviation is used to speed up the small target detection and improve detection accuracy. Then, density-based clustering helps to detect the shape of an object and can easily identify the centroid point. By integrating these two approaches, the actual target is selected with the smallest sum of intensities within a bounding box of a specific size. The proposed method is a novel small target detection algorithm that differs from the existing ones. We built over 300 datasets with various scenes and compared other algorithms. The experimental results demonstrated that the proposed algorithm is suitable for real-time applications and effective even when the target size is as small as 2 × 2 pixels. © ICROS 2023.