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Open AccessArticle
A Unified Preprocessing Pipeline for Noise-Resilient Crack Segmentation in Leaky Infrastructure Surfaces
by
Jae-Jun Shin
Jae-Jun Shin and
Jeongho Cho
Jeongho Cho *
Department of Electrical Engineering, Soonchunhyang University, Asan 31538, Republic of Korea
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(17), 5574; https://doi.org/10.3390/s25175574 (registering DOI)
Submission received: 30 July 2025
/
Revised: 2 September 2025
/
Accepted: 5 September 2025
/
Published: 6 September 2025
Abstract
Wet cracks caused by leakage often exhibit visual and structural distortions due to surface contamination, salt crystallization, and corrosion byproducts. These factors significantly degrade the performance of sensor- and vision-based crack detection systems. In moist environments, the initiation and propagation of cracks tend to be highly nonlinear and irregular, making it challenging to distinguish crack regions from the background—especially under visual noise such as reflections, stains, and low contrast. To address these challenges, this study proposes a segmentation framework that integrates a dedicated preprocessing pipeline aimed at suppressing noise and enhancing feature clarity, all without altering the underlying segmentation architecture. The pipeline begins with adaptive thresholding to perform initial binarization under varying lighting conditions. This is followed by morphological operations and connected component analysis to eliminate micro-level noise and restore structural continuity of crack patterns. Subsequently, both local and global contrast are enhanced using histogram stretching and contrast limited adaptive histogram equalization. Finally, a background fusion step is applied to emphasize crack features while preserving the original surface texture. Experimental results demonstrate that the proposed method significantly improves segmentation performance under adverse conditions. Notably, it achieves a precision of 97.5% and exhibits strong robustness against noise introduced by moisture, reflections, and surface irregularities. These findings confirm that targeted preprocessing can substantially enhance the accuracy and reliability of crack detection systems deployed in real-world infrastructure inspection scenarios.
Share and Cite
MDPI and ACS Style
Shin, J.-J.; Cho, J.
A Unified Preprocessing Pipeline for Noise-Resilient Crack Segmentation in Leaky Infrastructure Surfaces. Sensors 2025, 25, 5574.
https://doi.org/10.3390/s25175574
AMA Style
Shin J-J, Cho J.
A Unified Preprocessing Pipeline for Noise-Resilient Crack Segmentation in Leaky Infrastructure Surfaces. Sensors. 2025; 25(17):5574.
https://doi.org/10.3390/s25175574
Chicago/Turabian Style
Shin, Jae-Jun, and Jeongho Cho.
2025. "A Unified Preprocessing Pipeline for Noise-Resilient Crack Segmentation in Leaky Infrastructure Surfaces" Sensors 25, no. 17: 5574.
https://doi.org/10.3390/s25175574
APA Style
Shin, J.-J., & Cho, J.
(2025). A Unified Preprocessing Pipeline for Noise-Resilient Crack Segmentation in Leaky Infrastructure Surfaces. Sensors, 25(17), 5574.
https://doi.org/10.3390/s25175574
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