The Real-Time Pavement Distress Detection System Based on Edge-Cloud Collaborative Computing

Most advanced methods for road surface defect detection may have the issue of large network structures, making them impractical for implementation on mobile embedded systems. In this study, A lightweight road surface defect real-time intelligent detection system based on cloud-edge collaboration is proposed. The system comprises a lightweight feature-enhanced YOLO network (YOLO-LFE), a multi-object tracking network called ByteTrack, and is deployed on intelligent edge devices, enabling real-time defect detection and storage of uploaded defect information through edge computing modules and cloud systems. Built upon the latest YOLOv8 architecture, a lightweight MobileNetV3 network is first introduced as the backbone feature extraction component, resulting in a noteworthy decrease in parameters and computational intricacy. Subsequently, the Enhanced Spatial Pyramid Pooling (ESPP) method built upon the human visual perception system is applied to swap out the Spatial Pyramid Pooling Fusion (SPPF) method, thereby enhancing the model’s capability to capture features from tiny entities. Finally, an enhanced progressive feature fusion network is designed to further address the semantic gap issue during subsequent fusion of primary features. Experimental results on a self-made dataset and the public RDD2022 dataset show that the proposed detection model maintains high accuracy while requiring fewer parameters and less computational resources. Compared to YOLOv8, the model reduces the number of parameters by 32.5% and decreases computational requirements by 37%. This makes it more suitable for deployment on embedded devices. Additionally, the system has been field-tested on vehicles, thereby validating the effectiveness of the entire system.