Frequency-Gradient Collaborative Network With Channel Correction and Background Guidance for Infrared Small Target Detection

Infrared imaging exploits the thermal radiation characteristics of objects to transform infrared radiation into images. The scattering of thermal radiation degrades the resolution of infrared imaging, leading to low resolution outputs compared to visible light imagery. Consequently, in scenarios characterized by weak target features and complex backgrounds, existing methods still exhibit false alarms and missed detections. To address these challenges, we propose a novel Frequency-Gradient Collaborative Network with Channel Correction and Background Guidance (FGCBNet) for infrared small target detection. Specifically, we design an attention mechanism that integrates a multi-oriented gradient filtering module with a frequency domain learning module. The multi-oriented gradient filtering module leverages the inherent gradient characteristics of the target to suppress background noise in spatial domain, while the frequency domain learning module isolates high-frequency and low-frequency components to effectively separate target details from background interference. In addition, a self-correction channel attention module is developed to adaptively mitigate feature deviations across channels. Finally, a spatial-channel enhancement decoder progressively fuses deep and shallow features during up-sampling, employing background semantic guidance to further enhance target saliency. Experimental results demonstrated that the proposed method achieves superior performance on standardized public benchmarks.