A Method of Data Augmentation for Classifying Road Damage Considering Influence on Classification Accuracy

Tsuchiya, Haruki; Fukui, Shinji; Iwahori, Yuji; Hayashi, Yoshitsugu; Achariyaviriya, Witsarut; Kijsirikul, Boonserm

doi:10.1016/j.procs.2019.09.315

Cited by 24 publications

(5 citation statements)

References 4 publications

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“…Jenkins et al presented an encoder-decoder architecture to perform road crack detection, and the function of the encoder and decoder layers are used to reduce the size of input image to generate lower level feature maps, and obtain the resolution of the input data with up-sampling, respectively [62]. Tisuchiya et al proposed a data augmentation method based on YOLOv3 to perform crack detection, which can increase the accuracy effectively [63].…”

Section: Artificial Intelligencementioning

confidence: 99%

Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture

Fan

Chen

et al. 2020

Materials

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Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low- level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can extract and fuse different context sizes and different levels of feature maps than other algorithms.

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Section: Artificial Intelligencementioning

confidence: 99%

Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture

Fan

Chen

et al. 2020

Materials

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“…To address these challenges and achieve accurate bolt looseness predictions in datascarce scenarios, we propose a novel audio signal augmentation approach to amplify inadequate data. Though researchers employed several data augmentation methods for damage classification in structures [43][44][45][46], our study is the first to use an audio signal augmentation approach to enhance inadequate data to classify bolt looseness through CNN models. The proposed audio signal augmentation method is imperative for bolt looseness detection in complex bolted joints.…”

Section: Introductionmentioning

confidence: 99%

Addressing data scarcity using audio signal augmentation and deep learning for bolt looseness prediction

Chelimilla,

Chinthapenta,

Korla

2024

Smart Mater. Struct.

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Deep learning models such as convolutional neural networks (CNNs) encounter challenges, including instability and overfitting, while predicting bolt looseness in data-scarce scenarios. In this study, we proposed a novel audio signal augmentation approach to classify bolt looseness in the event of data deficiency using CNN models. Audio signals at varied bolt torque conditions were extracted using the percussion method. Audio signal augmentation was performed using signal shifting and scaling strategies after segmenting the extracted audio signals. The unaugmented and augmented audio signals were transformed into scalograms using the continuous wavelet transform approach to train the CNN models. Upon training with augmented datasets, a promising improvement in the loss and accuracy of the CNN models in recognizing bolt looseness was noticed. One of the significant observations from the current study is that the implementation of audio signal augmentation improved the extrinsic generalization ability of the CNN models to classify bolt looseness. A maximum increase of 73.5% to identify bolt looseness in novel data was exhibited as compared to without augmentation. Overall, a maximum accuracy of 94.5% to classify bolt looseness in unseen data was demonstrated upon audio signal augmentation. In summary, the results affirm that the audio signal augmentation approach empowered the CNN models to predict bolt looseness in data-deficient scenarios accurately.

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“…Considering the curve characteristics of cracks, Xie et al 29 treated crack detection as an edge detection task. With the development of object detection, Tsuchiya et al 42 adopted YOLOv3 43 with effective data augmentation methods depending on the class of road damage. However, few models have been developed for pixel-wise crack detection.…”

Section: Introductionmentioning

confidence: 99%

“…treated crack detection as an edge detection task. With the development of object detection, Tsuchiya et al 42 . adopted YOLOv3 43 with effective data augmentation methods depending on the class of road damage.…”

Section: Introductionmentioning

confidence: 99%

Method of convolutional neural network with hybrid attention for crack detection

Chen

2022

J. Electron. Imag.

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. In recent years, automatic image crack detection has become a critical task for ensuring the safety of various facilities. Many researchers utilized convolutional neural networks (CNNs) for crack detection. However, the existing CNN methods have the limitation of the receptive fields and struggle to establish long dependencies and global background information. Aiming to address this problem, we propose a CNN method with an attention module and recurrent mechanism. In terms of the backbone network, we propose a backbone network named Attention-VGG-19. In our network, we remove the last three fully connected layers of VGG-19 to expect the meaningful side output with different scales and to reduce the number of parameters. The fully connected layers are computationally intensive. The proposed network also provides integrated direct supervision for features of each convolutional stage. Then, to promote important features and suppress unimportant features, an attention module called CrackP attention is proposed. In the recurrent mechanism, the module can be unrolled into R loops to connect the path between one pixel and its neighboring ones. To prove the effectiveness of our proposed method, we evaluate it on four public crack datasets, DeepCrack, crack forest dataset (CFD), Crack500, and CrackTree260, on which it achieves F-score (F1) values of 0.876, 0.652, 0.692, and 0.593, respectively. After a guided filter is added, our method achieves F1 values of 0.878, 0.638, 0.681, and 0.526 on these four datasets.

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A Method of Data Augmentation for Classifying Road Damage Considering Influence on Classification Accuracy

Cited by 24 publications

References 4 publications

Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture

Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture

Addressing data scarcity using audio signal augmentation and deep learning for bolt looseness prediction

Method of convolutional neural network with hybrid attention for crack detection

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