Year: 2025
Type: article
Abstract: In an increasing demand for safe and more reliable infrastructure, early detection of structural defects in buildings is now considered to prevent a catastrophic failure. This paper discusses an AI-enhanced real-time crack detection system using the YOLO algorithm, one of the deep learning algorithms which can detect an object from the given images or video feeds in real time. The proposed system makes use of YOLO's object detection capability to detect and classify cracks in images or video feeds. This system aims to support the efficient and proactive monitoring of structure health by building managers and engineers. Delivering fast and accurate crack detection, this AI solution surpasses the traditional approaches that would normally raise the costs of maintenance and minimize the overall safety level of a building. Implementation of this methodology would help result in safer buildings and cost-efficient building management. Key Words:AI-enhanced crack detection ,YOLO algorithm Real- time monitoring ,Structural health, Building safety.
DOI: 10.55041/ijsrem41670
Author: M.Umar Farook
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4407800113