Comparative deep learning studies for indirect tunnel monitoring with and without Fourier pre-processing

Rosso, Marco Martino; Aloisio, Angelo; Randazzo, Vincenzo; Tanzi, Leonardo; Cirrincione, Giansalvo; Marano, Giuseppe Carlo

doi:10.3233/ica-230709

Cited by 12 publications

(6 citation statements)

References 62 publications

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“…These features are, in turn, fed into the classification layer for the prediction or classification of the input image. Commonly adopted CNN models for image classification, AlexNet [36], VGG [37], GoogleNet, and ResNet [38,39], are appropriate for image classification tasks.…”

Section: Dcnn Application In Segmentationmentioning

confidence: 99%

Evaluation of Infrared Thermography Dataset for Delamination Detection in Reinforced Concrete Bridge Decks

Ichi,

Dorafshan

2024

Applied Sciences

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Structural health monitoring and condition assessment of existing bridge decks is a growing challenge. Conventional manned inspections are costly, labor-intensive, and often risky to execute. Sub-surface delamination, a leading cause of deck replacement, can be autonomously and objectively detected using infrared thermography (IRT) data with developed deep learning AI models to address some of the limitations associated with manned inspection. As one of the most promising classifiers, deep convolutional neural networks (DCNNs) have not been utilized to their fullest potential for delamination detection, arguably due to the scarcity of realistic ground truth datasets. In this study, a common encoder–decoder semantic segmentation-based DCNN is adapted through domain adaptation. The model was tuned and trained on a publicly available dataset to detect subsurface delamination in IRT data collected from in-service bridge decks. The authors investigated the effect of dataset augmentation, class imbalance, the number of classes, and the effect of background removal in the training dataset, resulting in an overall number of seventy-five UNET models. Four out of five bridges were adopted for training and validation, and the fifth bridge was for testing. Most models averaged 80 iterations, and the training progress finally reached a training accuracy of 75% with a loss of about 0.6 without any overfitting. The result showed a substantial difference in the minimum and maximum values for the evaluated performance metrics (0.447 and 0.773 for global accuracy, 0.494 and 0.657 for mean accuracy, 0.239 and 0.716 for precision, 0.243 and 0.558 for true positive rate (TPR), 0.529 and 0.899 for true negative rate (TNR), 0.282 and 0.550 for F1-score. The results also indicated that the models trained on the raw annotated balanced dataset performed best for half of the metrics. In contrast, the models trained on raw data (with no dataset enhancement) performed better when only global accuracy was considered.

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Section: Dcnn Application In Segmentationmentioning

confidence: 99%

Evaluation of Infrared Thermography Dataset for Delamination Detection in Reinforced Concrete Bridge Decks

Ichi,

Dorafshan

2024

Applied Sciences

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“…Ye et al (2023) segmented cracks on slab tracks adopting a network with skip connections and estimated crack sizes. Rosso et al (2023) compared deep CNNs using Fourier transform as a preprocessing algorithm to detect anomalies in tunnel lining sensing data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Rosso et al. (2023) compared deep CNNs using Fourier transform as a preprocessing algorithm to detect anomalies in tunnel lining sensing data.…”

Section: Literature Reviewmentioning

confidence: 99%

Road crack detection interpreting background images by convolutional neural networks and a self‐organizing map

Yamaguchi,

Mizutani

2023

Computer aided Civil Eng

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The presence of road cracks is an important indicator of damage. Deep learning is a prevailing method for detecting cracks in road surface images because of its detection ability. Previous research works focused on supervised convolutional neural networks (CNNs) without non‐crack features or unsupervised crack analysis with limited accuracies. The novelty of this study is the addition of background classification. By increasing the number of non‐crack categories, CNNs are driven to learn non‐crack features and improve crack detection performances. Non‐crack images are preprocessed, and their features are extracted in an unsupervised way by a deep convolutional autoencoder. A self‐organizing map clusters features to obtain non‐crack categories. This study focusses on classification though the method can be adopted in parallel with the latest segmentation algorithms. Using common road crack datasets, modified deep CNN models significantly improved accuracy by 1%–4% and f‐measure by 3%–8%, compared to previous models. The modified visual geometry group (VGG) 16 showed the top‐level performance, 96% accuracy and 84%–85% f‐measure. The models drastically reduced false detection cases while maintaining their crack detection abilities.

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“…The main segmentation indicators of DeepLab v3 and U-Net are IoU, mIoU, and recall [45,46]. IoU is an indicator that evaluates the degree of overlap between the predicted and true boxes, which is the ratio of the overlap area between the predicted and true boxes to the union area.…”

Section: Evaluation Indicesmentioning

confidence: 99%

Deep-Learning- and Unmanned Aerial Vehicle-Based Structural Crack Detection in Concrete

Jin,

Zhang,

Chen

et al. 2023

Buildings

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Deep-learning- and unmanned aerial vehicle (UAV)-based methods facilitate structural crack detection for tall structures. However, contemporary datasets are generally established using images taken with handheld or vehicle-mounted cameras. Thus, these images might be different from those taken by UAVs in terms of resolution and lighting conditions. Considering the difficulty and complexity of establishing a crack image dataset, making full use of the current datasets can help reduce the shortage of UAV-based crack image datasets. Therefore, the performance evaluation of existing crack image datasets in training deep neural networks (DNNs) for crack detection in UAV images is essential. In this study, four DNNs were trained with different architectures based on a publicly available dataset and tested using a small UAV-based crack image dataset with 648 +pixel-wise annotated images. These DNNs were first tested using the four indices of precision, recall, mIoU, and F1, and image tests were also conducted for intuitive comparison. Moreover, a field experiment was carried out to verify the performance of the trained DNNs in detecting cracks from raw UAV structural images. The results indicate that the existing dataset can be useful to train DNNs for crack detection from UAV images; the TransUNet achieved the best performance in detecting all kinds of structural cracks.

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Comparative deep learning studies for indirect tunnel monitoring with and without Fourier pre-processing

Cited by 12 publications

References 62 publications

Evaluation of Infrared Thermography Dataset for Delamination Detection in Reinforced Concrete Bridge Decks

Evaluation of Infrared Thermography Dataset for Delamination Detection in Reinforced Concrete Bridge Decks

Road crack detection interpreting background images by convolutional neural networks and a self‐organizing map

Deep-Learning- and Unmanned Aerial Vehicle-Based Structural Crack Detection in Concrete

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