Application of Image Recognition Technology to Sound Spectrogram of Impact-Echo Method

Shimbo, Hiroshi; Mizobuchi, Toshiaki; Nojima, Junichiro

doi:10.12783/shm2019/32121

Cited by 1 publication

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“…With the advent of machine learning, there has been a significant shift towards enhancing the interpretative capabilities of impact-echo data. Machine learning has substantially improved the accuracy and reliability of data interpretation from impact-echo tests [8][9][10][11], leading to advancements in automating NDT and defect recognition in concrete structures [12], [13]. The evolution from machine learning to deep learning, particularly Convolutional Neural Networks (CNNs), has further expanded the possibilities in defect detection and classification [14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Enhancing non-destructive testing in concrete structures: a GADF-CNN approach for defect detection

Feng,

2024

J. meas. eng.

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This research introduces a novel approach for detecting defects in concrete structures. It utilizes the Gramian Angular Difference Field (GADF) in combination with a Convolutional Neural Network (CNN) enhanced by depthwise separable convolutions and attention mechanisms. The key contribution of this work is the use of GADF to transform one-dimensional impact-echo signals into two-dimensional images, thereby improving feature extraction and computational efficiency for analysis by the CNN. This advancement offers a new perspective in non-destructive testing technologies for concrete infrastructure. Comprehensive evaluation on a varied dataset of concrete structural defects reveals that our GADF-CNN model achieves an impressive test accuracy of 98.24 %, surpassing conventional models like VGG16, ResNet18, DenseNet, and ResNeXt50, and excelling in precision, recall, and F1-score metrics. Ultimately, this study enhances the integration of sophisticated image transformation techniques with deep learning, contributing to safer and more durable concrete infrastructure, and represents a noteworthy development in the field.

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Section: Introductionmentioning

confidence: 99%

Enhancing non-destructive testing in concrete structures: a GADF-CNN approach for defect detection

Feng,

2024

J. meas. eng.

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Application of Image Recognition Technology to Sound Spectrogram of Impact-Echo Method

Cited by 1 publication

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Enhancing non-destructive testing in concrete structures: a GADF-CNN approach for defect detection

Enhancing non-destructive testing in concrete structures: a GADF-CNN approach for defect detection

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