The System Operates by Capturing Images of the Wall Surface and Applying Advanced Image Processing Algorithms to Analyze the Visual Data

The main objective of this research is to leverage data and image processing technology to develop a system capable of detecting cracks in walls. The system aims to provide users with a convenient and user-friendly solution by utilizing principles from app development, enabling them to capture photos and assess the presence of wall cracks. To achieve this objective, various image processing techniques are employed, including the use of a Gaussian kernel, grayscale conversion, Sobel edge detection, and thresholding. These techniques are implemented using the Python programming language to develop and apply the image processing algorithms specifically designed for wall crack detection. By employing these techniques, the system can accurately identify cracks and assess the extent of damage in a house using preliminary data. The developed system offers significant advantages for potential homebuyers as it aids in assessing necessary repairs before making a purchase decision. By providing users with a tool to detect and evaluate wall cracks, they can make informed choices regarding the condition of the property and estimate the potential repair costs. This research aims to enhance the accuracy and efficiency of assessing wall cracks, ultimately benefiting individuals in their decision-making process when considering a property purchase.