Automated Pavement Cracks Detection and Classification Using Deep Learning

TL;DR

This paper presents a deep learning-based method using YOLOv5 and YOLOv8 for automated detection and classification of pavement cracks, aiming to improve accuracy and efficiency in highway asset management.

Contribution

It introduces a novel application of YOLO algorithms for pavement crack detection and classification, demonstrating their effectiveness under varying conditions.

Findings

Detection precision up to 67.3% under different illumination conditions

Reduces manual inspection time and costs

Enhances accuracy in asset condition assessment

Abstract

Monitoring asset conditions is a crucial factor in building efficient transportation asset management. Because of substantial advances in image processing, traditional manual classification has been largely replaced by semi-automatic/automatic techniques. As a result, automated asset detection and classification techniques are required. This paper proposes a methodology to detect and classify roadway pavement cracks using the well-known You Only Look Once (YOLO) version five (YOLOv5) and version 8 (YOLOv8) algorithms. Experimental results indicated that the precision of pavement crack detection reaches up to 67.3% under different illumination conditions and image sizes. The findings of this study can assist highway agencies in accurately detecting and classifying asset conditions under different illumination conditions. This will reduce the cost and time that are associated with manual inspection, which can greatly reduce the cost of highway asset maintenance.