Image Preprocessing Algorithm and Visual Implementation for Lane Lines Based on OpenCV

DOI

10.2991/978-94-6239-737-8_57How to use a DOI?

Keywords

Lane-line image preprocessing; OpenCV; real-time visualization; Gaussian filtering; CLAHE

Abstract

Image preprocessing is a critical prerequisite for computer-vision tasks such as lane-line detection and target classification, directly affecting the accuracy and efficiency of subsequent models. To address the common problems of random noise, uneven illumination, low contrast, and blurred lane contours in lane-line detection scenarios, 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 lane line images show that the proposed pipeline effectively removes noise, corrects illumination deviation and strengthens lane-line region contours. The signal-to-noise ratio (SNR) is improved by 30.6%, and the target contrast is improved by 48.1%, providing high-quality data support for subsequent lane-line 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  - Ting Li
AU  - Yifan Qu
AU  - Xue Yu
AU  - Hongrui Xu
AU  - Siying Qu
AU  - Miao Ma
PY  - 2026
DA  - 2026/08/18
TI  - Image Preprocessing Algorithm and Visual Implementation for Lane Lines Based on OpenCV
BT  - Proceedings of the 2026 5th International Conference on Art Design and Digital Technology (ADDT 2026)
PB  - Atlantis Press
SP  - 468
EP  - 474
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-737-8_57
DO  - 10.2991/978-94-6239-737-8_57
ID  - Li2026
ER  -