Image Preprocessing and Visual Implementation for Fabric Defects Based on OpenCV

DOI

10.2991/978-94-6239-737-8_66How to use a DOI?

Keywords

Fabric-defect image preprocessing; OpenCV; CLAHE

Abstract

Image preprocessing is a critical prerequisite for computer-vision tasks such as fabric-defect detection and target classification, directly affecting the accuracy and efficiency of subsequent models. To solve image noise, uneven illumination and blurred contours commonly observed in fabric-defect inspection scenes, this paper designs a complete preprocessing pipeline with seven core steps and implements real-time visualization based on Python and OpenCV. The pipeline includes grayscale conversion, Gaussian filtering, CLAHE contrast enhancement, adaptive threshold binarization, morphological closing, Canny edge detection and edge-morphology fusion. Experimental results on real fabric defect images show that the proposed pipeline effectively removes noise, corrects illumination deviation and strengthens defect regions contours. The signal-to-noise ratio (SNR) is improved by 32.3% and the target contrast is improved by 52.4%, providing high-quality data support for subsequent fabric-defect detection models such as YOLO.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Hongrui Xu
AU  - Yifan Qu
AU  - Ting Li
AU  - Xue Yu
PY  - 2026
DA  - 2026/08/18
TI  - Image Preprocessing and Visual Implementation for Fabric Defects Based on OpenCV
BT  - Proceedings of the 2026 5th International Conference on Art Design and Digital Technology (ADDT 2026)
PB  - Atlantis Press
SP  - 562
EP  - 568
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-737-8_66
DO  - 10.2991/978-94-6239-737-8_66
ID  - Xu2026
ER  -