2024/12/23 by Ravi Datta Rachuri, Duoduo Liao, Rachuri, Ravi Datta +5 · 1 citation
Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Geophysical Methods and Applications #Industrial Vision Systems and Defect Detection #Infrastructure Maintenance and Monitoring
paper · pdf · doi:10.48550/arxiv.2412.17968
openalex publication_date 2024/12/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents a pilot study introducing a multimodal fusion framework for the detection and analysis of bridge defects, integrating Non-Destructive Evaluation (NDE) techniques with advanced image processing to enable precise structural assessment. By combining data from Impact Echo (IE) and Ultrasonic Surface Waves (USW) methods, this preliminary investigation focuses on identifying defect-prone regions within concrete structures, emphasizing critical indicators such as delamination and debonding. Using geospatial analysis with alpha shapes, fusion of defect points, and unified lane boundaries, the proposed framework consolidates disparate data sources to enhance defect localization and facilitate the identification of overlapping defect regions. Cross-verification with adaptive image processing further validates detected defects by aligning their coordinates with visual data, utilizing advanced contour-based mapping and bounding box techniques for precise defect identification. The experimental results, with an F1 score of 0.83, demonstrate the potential efficacy of the approach in improving defect localization, reducing false positives, and enhancing detection accuracy, which provides a foundation for future research and larger-scale validation. This preliminary exploration establishes the framework as a promising tool for efficient bridge health assessment, with implications for proactive structural monitoring and maintenance.