Lidar based edge-detection for bridge defect identification

Haitao Bian; Libin Bai; Shen-En Chen; Sheng-Guo Wang

doi:10.1117/12.915264

4 April 2012 Lidar based edge-detection for bridge defect identification

Haitao Bian, Libin Bai, Shen-En Chen, Sheng-Guo Wang

Proceedings Volume 8347, Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2012; 83470X (2012) https://doi.org/10.1117/12.915264
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2012, San Diego, California, United States

Abstract

Previous visual damage detection on bridge structure based on eye-ball method is arbitrary and time-consuming for bridge management due to its heuristic nature. Commercial remote sensing (CRS), which has remarkable applications for geometric quantification, is suggested to supplement visual bridge inspection. Ground-based LIDAR is one of the remote sensing tools that have been successfully used in bridge evaluation. Most of the early measurement algorithms are developed based on the spatial information contained from the LIDAR data; this paper explores the potential of applying another important feature of the scan data: the reflectivity, to enhance the defect detection program. The addition of reflectivity in damage diagnostics is particularly useful for defect detection of curved surfaces. A damaged joint area and concrete beam were selected to verify the method. The study shows that the reflectivity of the LIDAR could be used to support the automatic defect detection in bridges by combining it with the current position-based only image processing algorithms.

Citation Download Citation

Haitao Bian, Libin Bai, Shen-En Chen, and Sheng-Guo Wang "Lidar based edge-detection for bridge defect identification", Proc. SPIE 8347, Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2012, 83470X (4 April 2012); https://doi.org/10.1117/12.915264

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