From street-view sensing to maintenance decisions: A knowledge-based engineering informatics framework for urban pavement defect assessment

Urban pavement defects, such as cracks and potholes, pose significant challenges to road safety and maintenance. Traditional pavement defect detection methods rely heavily on manual inspection, which is labor-intensive and time-consuming. Recent advances in deep learning have opened new opportunities for automating this process, particularly through the use of convolutional neural networks (CNNs). This paper presents an improved deep learning-based approach for detecting pavement defects using street view imagery. The proposed method leverages a customized dataset constructed from high-resolution street view images, incorporating both common and hazardous defects. The detection algorithm is based on an enhanced YOLOv8 model, optimized for handling low-resolution images and small defect targets. The model improvements include the introduction of a spatial-depth convolutional layer to preserve fine-grained information, a generalized feature pyramid network for better feature fusion, and a dynamic head with multi-task awareness for improved detection accuracy in complex urban environments. Experimental results demonstrate that the proposed model achieves superior performance in detecting pavement defects, with a mean Average Precision (mAP) improvement of 4.7% over the baseline model, while maintaining high inference speed. These findings suggest that the enhanced YOLOv8 model can be effectively applied to urban pavement maintenance, providing a reliable and efficient solution for large-scale defect detection. • Developed an enhanced YOLOv8 model for detecting pavement defects in urban environments using street view imagery. • Constructed a custom dataset with 3798 annotated images containing over 5600 crack and pothole targets. • Demonstrated robust detection accuracy under varying urban conditions, including low-resolution and complex backgrounds.