Saliency at the Helm: Steering Infrared Small Target Detection With Learnable Kernels

Infrared small target detection (ISTD) boasts extensive applications across civil and military domains, owing to its exceptional all-day performance. Neural network innovations have led to deep ISTD models that achieve heightened accuracy through extensive datasets. However, these general networks often fail to perceive the sensitivity of small targets and adopt heavy constructions to preserve potential target features, neglecting domain-specific insights and suffering from poor explainability. Our work seeks to rectify this by revisiting the saliency principles inherent to ISTD and developing a learnable local saliency kernel network (L2SKNet). This approach implements a learnable local saliency kernel module (LLSKM) that embodies the concept of “Center subtracts Neighbors,” guiding the network to capture the saliency features (points or edges). We enhance LLSKM by incorporating strategic dilation and structuring it hierarchically, which boosts its capability to capture multiscale infrared features while avoiding parameter explosion. In pursuit of efficiency, we also refine LLSKM into a more compact form by factorizing it into two orthogonal 1-D kernels, yielding a lightweight version. Heatmap visualizations and rigorous quantitative analyses corroborate the effectiveness of our local saliency-guided networks. Comprehensive testing reveals that L2SKNet variants outperform established baselines, demonstrating significant improvements in both visual and numerical assessments. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/fengyiwu98/L2SKNet</uri>.