Infrared Small Target Detection Algorithm Using an Augmented Intensity and Density-Based Clustering

In infrared search and tracking (IRST) systems, small target detection is challenging because IR imaging lacks feature information and has a low signal-to-noise ratio. The recently studied small IR target detection methods have achieved high detection performance without considering execution time. We propose a fast and robust single-frame IR small target detection algorithm while maintaining excellent detection performance. The augmented infrared intensity map based on the standard deviation speeds up small target detection and improves detection accuracy. Density-based clustering helps to detect the shape of objects and makes it easy to identify centroid points. By incorporating these two approaches, the proposed method has a novel approach to the small target detection algorithm. We have self-built 300 images with various scenes and experimented with comparing other methods. Experimental results demonstrate that the proposed method is suitable for real-time detection and effective even when the target size is as small as 2 pixels.