ABSTRACT
This study proposes an automated approach for detecting insufficient cover thickness in concrete hollow slab bridges by leveraging deep learning techniques and Ground Penetrating Radar (GPR) image data. Utilizing the You Only Look Once (YOLO) object detection algorithm, combined with Python-based post-processing scripts for data filtering and analysis, a robust non-destructive detection system was developed. This system effectively identifies areas with insufficient cover thickness by analyzing GPR-derived cross-sectional images. Compared to traditional manual inspections, the proposed method significantly enhances inspection efficiency, reduces costs, and minimizes human error. Experimental results across multiple datasets demonstrate the system’s adaptability and accuracy, demonstrating its scalability and applicability for comprehensive structural assessments.
