Abstract Nighttime urban waterlogging detection remains challenging because weak illumination, glare, and water-surface reflections often obscure the boundaries and features of waterlogged road regions. In addition, the limited availability of annotated nighttime samples restricts the generalization ability of data-driven models. To address these issues, this study develops a vision-based edge intelligence framework for nighttime urban waterlogging detection and segmentation. A diffusion-based augmentation strategy is first used to expand nighttime urban waterlogging samples. Then, a lightweight instance segmentation model, named Nocturnal-YOLOv11, is constructed by embedding an adaptive low-light enhancement module into the YOLOv11 framework. The model is further converted, quantized, and deployed on the RDK X3 edge computing platform using an INT8 inference scheme. Experiments on an independent real nighttime test set demonstrate that the proposed method improves low-light visual perception of waterlogged areas under weak illumination and reflective conditions while maintaining real-time edge inference capability. These results indicate its potential for practical nighttime waterlogging perception, urban water management, and emergency response applications. Data Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. References - Anik MSBM, An C, Li SS (2025) Evolution from the physical process-based approaches to machine learning approaches to predicting urban floods: A literature review. Environm Syst Res 14(15). https://doi.org/10.1186/s40068-025-00409-3 - Arshad B, Ogie R, Barthelemy J et al (2019) Computer vision and IoT-based sensors in flood monitoring and mapping: A systematic review. Sensors 19(22):5012. https://doi.org/10.3390/s19225012 - Baller SP, Jindal A, Chadha M et al (2021) Deepedgebench: Benchmarking deep neural networks on edge devices. In: Proceedings of the 2021 IEEE international conference on cloud engineering, pp 20–30. https://doi.org/10.1109/IC2E52221.2021.00016 - Feng H, Mu G, Zhong S et al (2022) Benchmark analysis of yolo performance on edge intelligence devices. Cryptography 6(2):16. https://doi.org/10.3390/cryptography6020016 - Guo C, Li C, Guo J