The author proposes a diffusion model framework for Infrared Small Target Detection to address target-level insensitivity by generating mask posterior distributions.
Proposing a diffusion model framework for IRSTD to address target-level insensitivity and designing a low-frequency isolation module in the wavelet domain.
A novel Scale and Location Sensitive (SLS) loss is proposed to improve infrared small target detection by addressing the limitations of existing loss functions in capturing scale and location information. A simple Multi-Scale Head is introduced to the plain U-Net (MSHNet) to leverage the SLS loss, achieving state-of-the-art performance without complex model structures.
The proposed Spatial-channel Cross Transformer Network (SCTransNet) leverages spatial-channel cross transformer blocks to effectively model the semantic differences between infrared small targets and complex backgrounds, enabling accurate detection of small targets.
The proposed Gradient Attention Network (GaNet) effectively extracts and preserves edge and gradient information of small infrared targets, while a global feature extraction module provides comprehensive background perception to improve detection performance.