Cracks in Time: Analyzing Structural Deterioration in Ancient Monuments

The preservation and analysis of ancient monuments, marked by the passage of time and structural deterioration, present a unique challenge in understanding the history embedded within their weathered surfaces. This research proposes a comprehensive approach to enhance the visual representation of these monuments by employing a combination of image processing techniques and advanced deep learning algorithms. The first component of methodology involves the application of Histogram Equalization, a classic image enhancement technique, to effectively boost contrast and reveal subtle details within the images of ancient monuments. This step aims to unveil the intricate patterns of structural deterioration caused by natural and anthropogenic factors. Following this, the Watershed Algorithm is employed to segment and identify distinct regions of interest within the images. By isolating cracks and damaged areas, this algorithm facilitates a more granular analysis of the structural decay, providing insights into the specific locations and extents of deterioration. To further refine the analysis and accommodate the intricacies of ancient monument imagery, this paper introduces the Fast R- CNN (Convolutional Neural Network) algorithm. This deep learning model is trained to recognize and classify different types of cracks and damages, offering a more nuanced understanding of the historical processes contributing to the structural decay of the monuments. By integrating these three approaches, the experimental result provides a holistic and detailed analysis of structural deterioration in ancient monuments with the accuracy of 95%. The combination of classical image processing techniques and cutting-edge deep learning algorithms offers a powerful toolset for researchers and conservationists seeking to unravel the mysteries hidden within the cracks of time.