Aiming at the problems of low efficiency, high cost and poor real-time performance of traditional pavement detection vehicles, a lightweight edge real-time detection method for low-grade pavement distresses is proposed. This method adopts a novel YOLO-Trip model to efficiently extract color and spatial features, and integrates TensorRT technology to achieve real-time detection on the edge. For the existing mileage measurement challenge, an IMU and GNSS self-calibrated high-frequency odometer is designed, combined with Kalman filter and linear interpolation algorithm to realize ultra-high-frequency mileage measurement. A low-power onboard edge computing platform is built to collect and detect road surface images in real time without additional power supply. In the mileage measurement comparison experiment, the maximum sampling error of the system is only 0.9% different from that of the wheel encoder in the speed range of 0~40 km/h, which is significantly better than the single GNSS scheme. The model comparison experiment shows that the YOLO-Trip model leads the benchmark model by in recall rate and average precision, while the parameter quantity and the computational load are reduced, which alleviates the edge computing pressure. The system can detect transverse cracks, longitudinal cracks, alligator cracks and potholes and other diseases in real time, and accurately record the location information, which is suitable for rural concrete roads and mountainous asphalt roads, providing data support for road maintenance.