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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM41670

AI-Enhanced Building Safety: Real-Time Crack Detection with YOLO

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM41670

Volume:

9 |

2 |

Feb 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM41670

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

M1

Mrs.R.Aruna 1

MF2

M.Umar Farook 2

P3

P.Vignesh 3

SK4

S.Santhosh Kumar 4

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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.

KEYWORDS

AI-enhanced crack detection YOLO algorithm Real- time monitoring Structural health Building safety.

Cite This Article

Mrs.R.Aruna 1, M.Umar Farook 2, P.Vignesh 3, S.Santhosh Kumar 4, "AI-Enhanced Building Safety: Real-Time Crack Detection with YOLO," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 2, Feb 2025. https://doi.org/10.55041/IJSREM41670

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Publication Date

2/20/2025

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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