From street-view sensing to maintenance decisions: A knowledge-based engineering informatics framework for urban pavement defect assessment
Year: 2026
Type: article
Abstract: 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.
Authors: Linchao Li, Bangxing Li, Jiazhen Liu, Bowen Du
Institutions: Shenzhen University, Beihang University
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,230
OpenAlex ID: W7150971886