AI-Powered Optimization and Integrity of Hydro Power Plants

This research addresses the critical task of crack detection, essential for maintaining the safety and integrity of various structures. Detecting cracks is vital for ensuring the longevity and reliability of infrastructures such as buildings, bridges, and roads. Traditional methods like manual visual inspections are often ineffective, subjective, and prone to human error. This paper introduces an automated crack detection approach using grayscale image processing techniques to improve accuracy. The methodology involves converting images to grayscale, enhancing contrast, and applying edge detection algorithms along with morphological operations to effectively identify and isolate cracks. The experimental findings demonstrate that this approach not only accurately detects cracks but also minimizes false positives and reduces computational complexity. This research offers a practical automated solution for crack detection and paves the way for future advancements, including real-time monitoring with machine learning for more comprehensive and scalable infrastructure health assessment.