A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A Deep Learning-Based Image Captioning for Automated Description of Structural Components Condition
Along with visual data, textual information on civil engineering projects can provide a rich source of expert experiences and technical knowledge for diagnosing structural damage causes and their countermeasures. By implementing a cutting-edge deep learning approach in SHM (Structural Health Monitoring), the visual assessment, along with the interrelation of structural components and their working condition, can be retrieved to provide efficient damage evaluation and generate professional description documentation. The study proposes a methodology of an image captioning architecture of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for generating condition assessments of structural components. The purpose of this study is to investigate the pragmatic implementation of image captioning technology in structural health monitoring scenarios, improving the quality of inspection, and addressing the labor shortage of conventional maintenance. The results indicate that the proposed method can provide automated coherent text descriptions of structural components and their working conditions, simplify the inspection process, and deliver efficient maintenance management.
A Deep Learning-Based Image Captioning for Automated Description of Structural Components Condition
Along with visual data, textual information on civil engineering projects can provide a rich source of expert experiences and technical knowledge for diagnosing structural damage causes and their countermeasures. By implementing a cutting-edge deep learning approach in SHM (Structural Health Monitoring), the visual assessment, along with the interrelation of structural components and their working condition, can be retrieved to provide efficient damage evaluation and generate professional description documentation. The study proposes a methodology of an image captioning architecture of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for generating condition assessments of structural components. The purpose of this study is to investigate the pragmatic implementation of image captioning technology in structural health monitoring scenarios, improving the quality of inspection, and addressing the labor shortage of conventional maintenance. The results indicate that the proposed method can provide automated coherent text descriptions of structural components and their working conditions, simplify the inspection process, and deliver efficient maintenance management.
A Deep Learning-Based Image Captioning for Automated Description of Structural Components Condition
Lecture Notes in Civil Engineering
Reddy, J. N. (editor) / Wang, Chien Ming (editor) / Luong, Van Hai (editor) / Le, Anh Tuan (editor) / Dinh, Nguyen Ngoc Han (author) / Ahn, Yong Han (author)
The International Conference on Sustainable Civil Engineering and Architecture ; 2023 ; Da Nang City, Vietnam
2023-12-12
8 pages
Article/Chapter (Book)
Electronic Resource
English
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