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A Method of Concrete Surface Crack Detection Using an Improved Convolutional Neural Network (CNN) Model

He, Zhexin / Zhang, Huan

This essay spotlights concrete crack detection in infrastructure maintenance, highlighting its importance for structural integrity, cost-effectiveness, and eco-consciousness. It delves into various detection methods and introduces an improved VGG-16-based deep learning model with batch normalization, P-ReLU activation, and Adam optimization for better training outcomes. Through experiments on the MendeleyData-CrackDetection dataset, the enhanced model outperforms the original. This study underscores the significance of hyperparameter optimization and algorithm choice in deep learning.

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A Method of Concrete Surface Crack Detection Using an Improved Convolutional Neural Network (CNN) Model

He, Zhexin / Zhang, Huan

This essay spotlights concrete crack detection in infrastructure maintenance, highlighting its importance for structural integrity, cost-effectiveness, and eco-consciousness. It delves into various detection methods and introduces an improved VGG-16-based deep learning model with batch normalization, P-ReLU activation, and Adam optimization for better training outcomes. Through experiments on the MendeleyData-CrackDetection dataset, the enhanced model outperforms the original. This study underscores the significance of hyperparameter optimization and algorithm choice in deep learning.

A Method of Concrete Surface Crack Detection Using an Improved Convolutional Neural Network (CNN) Model

He, Zhexin / Zhang, Huan

This essay spotlights concrete crack detection in infrastructure maintenance, highlighting its importance for structural integrity, cost-effectiveness, and eco-consciousness. It delves into various detection methods and introduces an improved VGG-16-based deep learning model with batch normalization, P-ReLU activation, and Adam optimization for better training outcomes. Through experiments on the MendeleyData-CrackDetection dataset, the enhanced model outperforms the original. This study underscores the significance of hyperparameter optimization and algorithm choice in deep learning.

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Titel:

A Method of Concrete Surface Crack Detection Using an Improved Convolutional Neural Network (CNN) Model

Weitere Titelangaben:

Lecture Notes in Civil Engineering

Beteiligte:

Xiang, Ping (Herausgeber:in) / Zuo, Liangdong (Herausgeber:in) / He, Zhexin (Autor:in) / Zhang, Huan (Autor:in)

Kongress:

International Prefabricated Building Seminar on Frontier Technology and Talent Training ; 2023 ; Chongqin, China

Erschienen in:

Novel Technology and Whole-Process Management in Prefabricated Building ; Kapitel: 36 ; 335-345

Lecture Notes in Civil Engineering ; 382

Erscheinungsdatum:

20.07.2024

Format / Umfang:

11 pages

ISBN:

978-981-97-5108-2 , 978-981-97-5107-5

ISSN:

2366-2565 , 2366-2557

DOI:

https://doi.org/10.1007/978-981-97-5108-2_36

Medientyp:

Aufsatz/Kapitel (Buch)

Format:

Elektronische Ressource

Sprache:

Englisch

Schlagwörter:

concrete crack detection , an improved VGG-16 model , Adam optimziation Engineering , Building Construction and Design , Computer-Aided Engineering (CAD, CAE) and Design

Lizenzbestimmungen:

http://creativecommons.org/licenses/by/4.0/

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