Spatially Adaptive and Cross-Modal Differential Enhancement Network for Multispectral Object Detection

Multispectral object detection in complex urban environments remains a challenging task due to severe thermal diffusion, geometric variations, and modality-specific background noise. To address these issues, we propose the Spatially Adaptive and Cross-Modal Differential Enhancement Network (SACDNet), a novel framework featuring three core components: Spatially Adaptive Modulated Convolution (SAMC), Cross-Modal Differential Enhancement (CMDE), and Illumination-Guided Geometric Loss (IGGL). SAMC effectively adapts to target deformations by dynamically adjusting the shape of the receptive field. CMDE utilizes cross-modal channel attention to adaptively emphasize reliable structural representations. Furthermore, IGGL modulates the geometric penalty based on environmental illumination to mitigate the influence of noisy predictions. Experimental evaluations on the FLIR-align dataset demonstrate the effectiveness of our approach. Compared to the state-of-the-art (SOTA) methods, SACDNet achieves a significant improvement, reaching a mean Average Precision (mAP) of 37.2\%.