ISTDU-Net: Infrared Small-Target Detection U-Net

Abstract

The infrared small-target lacks effective information such as shape and texture, so it is difficult to detect small-target effectively. In order to solve this problem, a new deep learning network is proposed: Infrared Small-target Detection U-Net (ISTDU-Net). ISTDU-Net is a deep learning network based on a U-shaped structure. It converts a single frame infrared image into a target probability likelihood map of image pixels. ISTDU-Net not only introduces feature map groups in network down-sampling, sensing, and enhancing the weights of small target feature map groups to improve the characterization ability of small targets; but also introduces a fully connected layer in jump connection to suppress a large number of backgrounds with similar structures from the global receptive field, thus improving the contrast between targets and backgrounds. Experimental results show that the ISTDU-Net proposed in this letter can detect small infrared targets in complex backgrounds. Compared with other algorithms, ISTDU-Net has a better receiver operating characteristic (ROC) curve with a low false alarm rate, which the area under curve (AUC) value is 0.9977.