CMAF-Net: A novel multi-scale enhanced cross-modal adaptive fusion network for RGB-T object detection

Object detection in real-world settings frequently suffers from low-visibility challenges, such as backlighting, rain, snow, and occlusion as well as large variations in object scale. To address these issues, we present a novel Multi-scale Enhanced Cross-Modal Adaptive Fusion Network for RGB-T object detection, called CMAF-Net, which improves cross-modal fusion, multi-scale contextual modeling, detection head design, and localization loss optimization. In this framework, an Adaptive Gated Fusion Module (AGFM) is introduced at the mid-level fusion stage to strengthen the information exchange and complementary representation between the visible and infrared branches. In addition, a Multi-scale Contextual Attention Fusion module (MCAF) is used to strengthen multi-scale contextual modeling. An Adaptive Multi-scale Fusion Head (AMFH) is also designed to adaptively aggregate features across different scales, thereby improving detection performance for objects of varying sizes. During training, a Dynamic Weighted Intersection-over-Union loss (DW-IoU) is introduced to optimize bounding box regression, improving localization stability and regression accuracy for samples of varying quality through dynamic focusing and center-distance constraints. To validate the model’s performance, systematic experiments were conducted on three datasets: M3FD, LLVIP and VEDAI. The results demonstrate that CMAF-Net exhibits superior performance compared to other state-of-the-art object detection methods while maintaining a good balance between detection accuracy and computational cost. The experimental results also indicate that CMAF-Net can preserve robust detection capability in difficult scenarios, including rainy, foggy, and cluttered environments, exhibiting good environmental adaptability and robustness.