Frequency-Aware Contextual Feature Pyramid Network for Infrared Small-Target Detection

Abstract

Due to the absence of detailed information, such as texture, shape, and color, detecting infrared small targets remains a challenging problem. While existing model-driven and data-driven approaches have made some progress, they still struggle to effectively exploit global contextual information and frequency-specific details. In this letter, we introduce a frequency-aware contextual feature pyramid network (FACFPNet) to address these limitations in infrared small-target detection. Specifically, we first estimate the correlation between high- and low-frequency feature representations within an encoder-decoder framework based on the ResNet-18 backbone. This is achieved through the contextual fine-grained block (CFGB), which effectively combines local fine-grained features with global semantic information for enhanced contextual feature modeling. Next, we propose a frequency-aware attention module (FAAM) to address the underutilization of prior frequency knowledge in infrared small targets, thereby improving the preservation of these features. This module enhances global contextual representation by more effectively extracting high- and low-frequency information. Finally, during the decoding stage, shallow fine-structure information is interactively fused with deep semantic features through the asymmetric enhancement fusion module (AEFM), which strengthens the representation of small targets and improves information retention. Experimental results on three publicly available datasets, SIRST-Aug, MdvsFA, and IRSTD-1K, demonstrate that our method achieves superior detection performance.