Статья

Automated Pavement Cracks Detection and Classification Using Deep Learning

Selvia NafaaMinia University,Computers and Systems Department,Minia,EgyptKarim AshourMinia University,Computers and Systems Department,Minia,EgyptRana MohamedArab American University,Civil Engineering Department,Jenin,PalestineHafsa EssamMinia University,Computers and Systems Department,Minia,EgyptDoaa EmadMinia University,Computers and Systems Department,Minia,EgyptMohammed ElhenawyQueensland University of Technology,CARRS-Q,Brisbane,AustraliaHuthaifa I. AshqarArab American University,Civil Engineering Department,Jenin,PalestineAbdallah A. HassanMinia University,Computers and Systems Department,Minia,EgyptTaqwa I. AlhadidiAl-Ahliyya Amman University,Civil Engineering Department,Amman,Jordan

2024en

ABI

Аннотация

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.

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Идентификаторы

DOI: 10.1109/icmi60790.2024.10586098

Цитирования и источники

Цитирований: 4Использованных источников: 0

Показатели — AkademScholar