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@article{174144,
        author = {Yogeshwaran R and Karuppasamy S and Muniesh Vijay J and Asir D},
        title = {Wall Crack Detection Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4539-4541},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174144},
        abstract = {Structural integrity assessment is crucial for ensuring the safety and longevity of buildings. Manual crack detection methods are often labor-intensive, subjective, and prone to human errors. This paper presents an automated system leveraging deep learning techniques to detect wall cracks from images efficiently. Convolutional Neural Networks (CNNs) are employed for feature extraction and classification. The proposed system demonstrates superior accuracy and efficiency compared to traditional manual and image-processing-based crack detection methods. The study also highlights the robustness of the model under varying lighting and texture conditions.},
        keywords = {CNN, Deep Learning, Image Processing, Structural Health Monitoring, Wall Crack Detection.},
        month = {March},
        }