A lightweight design method for internal pavement crack recognition models using ground penetrating radar

Yu, X, Dong, C, Li, C, Li, D, Chen, C, Zhu, H and Gao, Y ORCID: 0000-0001-7310-1476 (2026) A lightweight design method for internal pavement crack recognition models using ground penetrating radar. Computer Aided Civil and Infrastructure Engineering, 43. ISSN 1093-9687

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Abstract

Transverse cracks are among the most prevalent forms of damage in Semi-rigid asphalt pavements, traditionally the crack identified through manual methods that are time-consuming and suffer from poor consistency. To improve the efficiency of crack disease identification, this study employs a self-developed high-speed and high-precision 3D ground-penetrating radar to characterize internal crack defects. The You Only Look Once-v8n algorithm is enhanced through lightweight design, optimizing its backbone network and loss function while incorporating an attention mechanism to strengthen crack feature extraction, detection accuracy, and training stability. Practical engineering evaluations demonstrate that the algorithm reduces manual identification time by 50%, with over 60% of results achieving high expert validation rates. The missed detection counts for visible cracks are comparable to manual methods. This algorithm enables automated identification of internal cracks in pavement rehabilitation projects.

Item Type:	Article
Uncontrolled Keywords:	Internal Pavement Cracks; 3D Ground Penetrating Radar; YOLO Algorithm; Lightweight Model Design; Automated Crack Detection; 4005 Civil Engineering; 40 Engineering; 0905 Civil Engineering; Civil Engineering; 4005 Civil engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TE Highway engineering. Roads and pavements
Divisions:	Engineering and Built Environment
Publisher:	Elsevier
Date of acceptance:	15 February 2026
Date of first compliant Open Access:	28 August 2026
Date Deposited:	28 Aug 2026 12:17
Last Modified:	28 Aug 2026 12:17
DOI or ID number:	10.1016/j.cacaie.2026.100028
URI:	https://researchonline.ljmu.ac.uk/id/eprint/29220

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