Remote sensing object detection (RSOD) requires compact backbones capable of preserving weak geometric cues under extreme scale variation and background clutter. Fixed structural operators provide complementary contour and frequency responses without introducing learnable operator coefficients. However, directly inject...
Wei Lu, Jun-Jie Li, Fei-Fei Sang et al.· 0 citations
Infrared small-target detection is widely used in early warning and remote monitoring. The targets are usually extremely weak, extremely small, and easily obscured by the chaotic background. However, existing methods still have poor detection accuracy in low-contrast and highly interfering scenarios, and many methods r...
Hao-Zhe Wang, Si-Bao Chen· Journal of Physics, Conferen...· 0 citations
Road extraction remains a challenging task due to irregular shapes, varying widths, occlusions, and complex terrain. Existing models often fail to address key issues, such as extraction interruptions caused by trees, buildings, or shadows, and the difficulty of distinguishing road edges from surrounding features under...
An end-to-end framework for weakly supervised change detection is proposed that effectively improves weakly supervised change detection performance and introduces a Reliability-Aware Contrastive Learning strategy to enhance the separability of change representations.
Die-Die Liu, Si-Bao Chen· Journal of Physics, Conferen...· 0 citations
This work introduces a Multi-Domain Attention Module (MDAM) to integrate spatial features with frequency-domain global context, and a Dynamic Frequency-domain Fusion Module (DFFM) that employs dynamic filters for detail refinement, noise suppression, and local brightness correction.
Yi-Fan Wang, Si-Bao Chen· International Conference on...· 0 citations
