Thermographic Laplacian-pyramid filtering to enhance delamination detection in concrete structure
Chongsheng Cheng, Ri Na, Zhigang Shen

TL;DR
This paper introduces a novel thermographic image filtering technique using Laplacian of Gaussian to improve delamination detection in concrete structures, effectively handling environmental noise and non-uniform heat distribution.
Contribution
The paper presents an empirical filtering method based on Laplacian of Gaussian that enhances thermal image analysis for delamination detection, outperforming existing approaches in various conditions.
Findings
Significant performance improvement over conventional methods.
Capable of detecting multiple delamination sizes from a single image.
Robust to non-uniform temperature distributions.
Abstract
Despite decades of efforts using thermography to detect delamination in concrete decks, challenges still exist in removing environmental noise from thermal images. The performance of conventional temperature-contrast approaches can be significantly limited by environment-induced non-uniform temperature distribution across imaging spaces. Time-series based methodologies were found robust to spatial temperature non-uniformity but require the extended period to collect data. A new empirical image filtering method is introduced in this paper to enhance the delamination detection using blob detection method that originated from computer vision. The proposed method employs a Laplacian of Gaussian filter to achieve multi-scale detection of abnormal thermal patterns by delaminated areas. Results were compared with the state-of-the-art methods and benchmarked with time-series methods in the case…
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