Encoder-Decoder Architectures for Crack Detection on Surfaces: A Deep Learning Approach

The building and maintenance of structures like roads, expressways, buildings, skyscrapers, and so forth requires a sizable workforce as well as a sizable financial investment in the fields of civil engineering and construction. Following completion, it is necessary to regularly check for deformatio...

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Bibliographic Details
Published in:Ingénierie des systèmes d'Information Vol. 30; no. 2; pp. 495 - 503
Main Authors: Sakshi, Vijay, Richa, Lodhi, Sachin, Noonia, Ajit, Berar, Gagandeep, Kumar, Ajay
Format: Journal Article
Language:English
Published: Edmonton International Information and Engineering Technology Association (IIETA) 01.02.2025
Subjects:
Accuracy
Algorithms
Cost control
Cracks
Datasets
Deep learning
Deformation
Encoders-Decoders
Fractures
Localization
Machine learning
Neural networks
Nuclear power plants
Real time
Research methodology
Road maintenance
Skyscrapers
ISSN:1633-1311, 2116-7125
Online Access:Get full text
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Summary:The building and maintenance of structures like roads, expressways, buildings, skyscrapers, and so forth requires a sizable workforce as well as a sizable financial investment in the fields of civil engineering and construction. Following completion, it is necessary to regularly check for deformations such as fractures, the structure's outermost layer peeling off, rusting, etc. To maintain the safety of both human life and the building itself, such deformations must therefore be continuously monitored and repaired. We present a neural network-based method to find cracks in such structures in addition to physical inspection. Using 2500 images as training data, the model had an accuracy of 95.0%; on the validation set, it had a mean IoU score of 83%. The proposed method also demonstrates superior performance, achieving a 15% increase in prediction accuracy when compared to state-of-the-art methods, thereby illustrating its worth in real-time applications.
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ISSN:1633-1311
2116-7125
DOI:10.18280/isi.300221