To address the issues of high parameter count and computational complexity in existing traffic surveillance detection models, which limit their deployment on edge devices due to hardware resource constraints, this study proposes a lightweight network architecture-based road surveillance detection model by specifically modifying the YOLOv8 model. In the backbone network, the minimalist architecture VanillaNET is introduced to replace the intermediate part of the original network for feature extraction, significantly reducing the model′s parameter count and overall computational complexity. The advantages of FasterNet are combined with the EMA attention mechanism and applied to the C2f module in the backbone network, effectively reducing memory access and enhancing the model′s detection capability. Additionally, the G-SPPCSPC module is proposed by integrating SPPCSPC with grouped convolutions, improving the model′s ability to extract multi-scale information under varying surveillance perspectives. Finally, in the neck network, the lightweight attention mechanism MLCA is incorporated into the C2f module to reduce interference from irrelevant background information in road surveillance detection. Experimental results show that the improved model reduces the parameter count by 53.3%, model size by 51.3%, and computational complexity by 48.1%, while achieving a mAP50/% of 93.7% and an FPS of 280.5. The model maintains high detection accuracy and speed while significantly reducing parameter count and computational complexity, making it suitable for deployment on edge devices and demonstrating high practical value.