Data of A Fast Detection Method Using Anisotropic Guidance for Infrared Small Target Under Complex Scenes

The development of intelligent control and visual navigation, along with the ongoing increase in imaging distance, has led to realistic demands for weak and small target detection. However, figuring out how to reduce the interference of various complex factors while achieving a high detection rate and low false alarm rate for real-time infrared small target detection is still a significant challenge. We propose a computationally simple single-frame infrared small target (SIRST) fast detection method applicable to complex scenes. First, a novel anisotropy filter bank suitable for small target feature extraction is constructed and modified with a point spread function (PSF). This makes it possible to map the salient features and spatial features of the target in the spatial domain using the imaging mechanism. We then combined two sets of feature parameters using the filtered features, including isotropy measure and directional energy (DE) factors, to enable the detection of small targets. In this dataset, we provide the effect of the proposed method on several typical open source datasets and compare it quantitatively with several other sets of state-of-the-art algorithms using 3D-ROC and runtime statistics. Experimental findings showed that the algorithm performs well in scenarios involving complicated clouds, urban ground, and vegetation. The software also achieved excellent real-time performance because of its straightforward computational design.