A Machine Learning Approach for Automated Detection and Classification of Cracks in Ancient Monuments using Image Processing Techniques

Abstract

Stone monuments stand as enduring testaments to human history and cultural heritage, yet they are susceptible to deterioration over time. In this paper, we propose a comprehensive approach for the automated detection and classification of cracks in ancient monuments, integrating machine learning and advanced image processing techniques. Our method addresses the pressing need for efficient and objective assessment of structural integrity in these invaluable artifacts. The proposed algorithm begins with preprocessing steps, including image enhancement using adaptive histogram equalization to improve crack visibility. Subsequently, feature extraction techniques such as Grey Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) are applied to capture essential characteristics of crack patterns. Central to our approach are the Back Propagation Neural Network (BPNN) and Improved Support Vector Machine (ISVM) classifiers, which are trained on the extracted features to detect and classify cracks with high accuracy. The BPNN learns complex relationships between input features and crack types, while the ISVM leverages a margin-based approach for robust classification. Through extensive experimentation on a diverse dataset of ancient monuments, we demonstrate the effectiveness of our approach in accurately identifying and categorizing cracks. The proposed method offers a scalable and objective solution for monitoring the structural health of ancient monuments, contributing to proactive conservation efforts and the preservation of cultural heritage.