Maintenance-Value Ranking of Pavement Defects from Multi-View Street Imagery
DOI:
https://doi.org/10.66238/smsrj.v4i2.73Keywords:
Pavement Defects, Multi-View Imagery, Maintenance-Value Ranking, Infrastructure ManagementAbstract
The structural integrity of road networks is paramount to global economic productivity and public safety. Traditional methods for assessing pavement conditions rely predominantly on manual inspections or single-view vehicular camera systems, which often fail to capture the true depth, context, and severity of road surface anomalies. This paper presents a novel framework for the automated maintenance-value ranking of pavement defects utilizing multi-view street imagery. By integrating data from overlapping camera angles, the proposed system transcends simple defect detection, moving towards a comprehensive evaluation of maintenance urgency based on defect morphology, spatial distribution, and potential risk to infrastructure longevity. The methodology encompasses extensive data acquisition, multi-view spatial alignment, and a bespoke ranking algorithm that prioritizes repair interventions based on a computed maintenance-value index. Through rigorous textual description of the experimental setup and subsequent data analysis, this research demonstrates that multi-view imagery significantly enhances the accuracy of severity classification compared to monocular approaches. The results indicate a high correlation between the automated rankings and ground-truth expert assessments, proving the viability of this approach for municipal asset management. This framework offers transportation agencies a highly efficient, scalable, and objective tool to allocate limited maintenance budgets effectively.
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