Performance Analysis on Surface Crack Detection in Buildings & Bridges Using Image Processing Method

Surface crack detection in buildings and bridges is critical for ensuring structural integrity and preventing potential failures. It presents a performance analysis of surface crack detection using image processing techniques, offering an efficient and non-invasive solution. Traditional manual inspection methods are labor – intensive, subjective, and prone to human error, making automated systems more desirable. In this study, we utilize digital image processing techniques such as edge detection, thresholding, and morphological operations to identify and quantify surface cracks. Key performance indicators such as detection accuracy, computational efficiency, and robustness to varying environmental conditions are evaluated. The experimental results demonstrate the system’s effectiveness in detecting fine cracks with high accuracy and low false positive rates, providing a reliable alternative for infrastructure maintenance. Additionally, the proposed method reduces inspection time and improves decision-making in the assessment of structural health. This study contributes to advancing automated crack detection systems, which are crucial for modernizing maintenance practices.