RDSHNet

Infrared imaging exhibits strong environmental robustness in low-visibility scenarios such as nighttime and rainy/foggy conditions; however, due to low signal-to-noise ratio, blurred boundaries, scarce textures, and pronounced background clutter, real-time vehicle detection still faces dual challenges: weakened structural details and unreliable high-level semantic representations. To achieve a better trade-off between accuracy and efficiency, this paper proposes RDSH-Net, a Real-time Detail-Semantic Hybrid Network for real-time infrared vehicle detection.红外成像（infrared imaging）在夜间、阴雨/雾天等低能见度场景中具备优异的环境鲁棒性；然而受限于低信噪比、边界模糊、纹理匮乏以及显著的背景杂波，实时车辆检测仍面临双重挑战：结构细节弱化与高层语义表征可靠性不足。为实现精度与效率的更优权衡，本文提出RDSH-Net——一种面向实时红外车辆检测的细节-语义混合网络（Real-time Detail-Semantic Hybrid Network）