Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{178088,
        author = {M.kalyani and Dr.k.Padmaja Devi and K. Menila and K. Keerthi},
        title = {WALL CRACK DETECTION USING PYTHON},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {6752-6756},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178088},
        abstract = {Wall crack detection is an important activity in structural health monitoring and building maintenance. Manual inspection tends to be time-consuming, prone to errors, and not efficient for big infrastructures. This project suggests an automated wall crack detection system based on Python and image processing. Utilizing libraries like OpenCV and machine learning models like Convolutional Neural Networks (CNNs), the system can effectively detect and classify cracks from images of walls. Preprocessing operations such as grayscale, edge detection, and morphological transformation improve crack characteristics prior to classification. The method addresses enhancements in the accuracy and efficiency of crack detection, minimizing human labor, and enabling timely maintenance to avoid structural failure. The system can be scaled to real-time applications via mobile or drone-based image acquisition for wider and faster area coverage in inspection.},
        keywords = {},
        month = {May},
        }