Road waterlogging is regarded as a widespread traffic safety hazard that can easily lead to vehicle loss of control and then trigger traffic accidents. Accurate identification of road waterlogging is limited by challenges such as complex road surface textures， variable lighting conditions， and the diverse morphology of puddle itself. To address these issues， REL-YOLO （Reflection-aware Enhanced Lightweight YOLO）， a lightweight road waterlogging detection network integrating reflection perception features， was proposed on the basis of YOLOv11n. First， the Reflection-Aware Feature Enhancement （RAFE） module was designed to fuse shallow texture and deep semantic features effectively， and specular reflection interference was suppressed through an adaptive noise weighting mechanism. Second， the C3k2_Enhanced module was designed by combining grouped convolution with a lightweight channel attention mechanism， so that the number of parameters was reduced while the capture capability of fine water flow features was enhanced. Finally， a Lightweight Shared Detail Enhanced Convolutional Detection head （Detect_LSDECD） was introduced， and multi-scale feature fusion was optimized through shared convolution and learnable scale parameters. Experimental results show that compared with the original YOLOv11n model on the Road waterlogging dataset， REL-YOLO has recall improved by 3.2 percentage points， mAP@50 improved by 2.0 percentage points， the number of model parameters reduced by 13.1%， and floating-point operations reduced from 6.3 GFLOPs to 4.3 GFLOPs. While maintaining high real-time performance， REL-YOLO achieves a balance between accuracy and efficiency.