  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.
