Road disease detection is crucial for traffic safety and road maintenance, but existing algorithms generally suffer from low detection accuracy, high computational costs, and difficulty in deploying on mobile devices. To address these issues, we propose a lightweight multi-scale road disease detection algorithm LMR-YOLO-P based on YOLOv8n. By designing a multi-scale group conv module to adapt to the variable sizes of road diseases, and constructing a light weight shared detection head to reduce computational costs while preserving fine details, introducing receptive field attention convolution RFAConv to enhance global information capture capability, combining the DFP module and efficient local attention mechanism to build a SAC module for enhanced multi-scale feature fusion, and finally utilizing the layer-adaptive sparsity for the magnitude-based pruning method to further compress the model. Experimental results show that on the RDD2022 dataset, the algorithm improved mAP50 by 1.8% compared to the YOLOv8n network, while reducing parameter count and computational cost by 46% and 40% respectively, successfully achieving lightweight and real-time high-precision detection of road diseases, providing an effective tool for intelligent road maintenance.