Infrared Small Target Detection via Diverse Feature Harmonization

Infrared small target detection (IRSTD) faces significant challenges due to low signal-to-noise ratios, poor contrast in infrared images, and the tendency for small and dim targets to be obscured by cluttered backgrounds. These factors complicate the extraction of diverse and effective information for target detection, with existing encoder-decoder architectures often causing irreversible loss of small target features through continuous downsampling, resulting in missed and false detections. To address these challenges, we propose the diverse feature capture and harmonization network (DFCHNet), which learns and harmonizes diverse features through multiple encoding paths. DFCHNet includes an infrared image reconstruction branch running in parallel with the detection branch, preserving small target information and reducing feature loss via complementary contextual encoding. Additionally, we introduce Fourier transform convolution (FTConv) to capture target edges while suppressing background noise. A cross-layer feature autonomous selection (CFAS) method adaptively harmonizes cross-layer features. We also propose a coordinate calibration (CC) loss function and a two-stage training strategy to refine predicted target positions. The experimental results on three IRSTD datasets demonstrate that DFCHNet outperforms current state-of-the-art (SOTA) methods. Our code is available at https://github.com/BJZHOU2000/DFCHNet.