Infrared Small Target Detection Method Based on High-Low-Frequency Semantic Reconstruction

The small scale and weak intensity of infrared (IR) small targets result in extremely sparse target features, making small target detection in complex environments extremely susceptible to strong background noise interference. Most existing detection methods only focus on sparse feature extraction of targets, while ignoring the problem of strong background clutter interference, which leads to significant limitations in detection performance in complex scenes. Therefore, to address this issue, we propose a new IR small target detection network based on high- and low-frequency semantic reconstruction, namely the HLSR-net. In the HLSR-net, we draw inspiration from the frequency characteristics differences between targets and backgrounds in IR images, and use the proposed high- and low-frequency semantic reconstruction strategy to suppress background clutter interference, effectively extracting sparse features of targets. First, in the latent feature space of the input image, high-frequency target and low-frequency background features are extracted separately through fast Fourier frequency transformation. Second, by utilizing the difference in frequency characteristics between small targets and background clutter, target enhancement and background suppression are respectively carried out through the designed high- and low-frequency reconstruction networks. Finally, to further enrich the feature expression of the target, we designed a fusion output strategy to achieve detection of small targets. Sufficient experimental results indicate that the proposed HLSR-net achieves the performance of SOTA on the benchmark dataset, with an accuracy improvement of 6%.