Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging

Kang, Dongho; Cha, Young‐Jin

doi:10.1111/mice.12375

Cited by 295 publications

(166 citation statements)

References 38 publications

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“…Particularly for aerial imagery, however, additional issues may arise and will need to be solved. Kang and Cha [7] highlight several challenges that need to be addressed before applying our approach outside of a manufacturing setting.…”

Section: Introductionmentioning

confidence: 99%

Automatic classification of defective photovoltaic module cells in electroluminescence images

et al. 2019

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Electroluminescence (EL) imaging is a useful modality for the inspection of photovoltaic (PV) modules. EL images provide high spatial resolution, which makes it possible to detect even finest defects on the surface of PV modules. However, the analysis of EL images is typically a manual process that is expensive, time-consuming, and requires expert knowledge of many different types of defects.In this work, we investigate two approaches for automatic detection of such defects in a single image of a PV cell. The approaches differ in their hardware requirements, which are dictated by their respective application scenarios. The more hardware-efficient approach is based on hand-crafted features that are classified in a Support Vector Machine (SVM). To obtain a strong performance, we investigate and compare various processing variants. The more hardware-demanding approach uses an end-to-end deep Convolutional Neural Network (CNN) that runs on a Graphics Processing Unit (GPU). Both approaches are trained on 1,968 cells extracted from high resolution EL intensity images of mono-and polycrystalline PV modules. The CNN is more accurate, and reaches an average accuracy of 88.42 %. The SVM achieves a slightly lower average accuracy of 82.44 %, but can run on arbitrary hardware. Both automated approaches make continuous, highly accurate monitoring of PV cells feasible.

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Section: Introductionmentioning

confidence: 99%

Automatic classification of defective photovoltaic module cells in electroluminescence images

et al. 2019

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“…The rapid development of Machine Learning (ML) and Deep Learning (DL) technologies and great improvement of Graphics Processing Unit (GPU) hardware motivate further developments and applications toward Artificial Intelligence (AI) and Intelligence Augmentation (IA) research area in many fields of science and engineering. Even in the traditional Civil and Structural Engineering areas, increased number of studies related to ML and DL have been taking place and resulting in promising findings (Kang & Cha, ; Lin, Nie, & Ma, ; Maeda, Sekimoto, Seto, Kashiyama, & Omata, ; Nabian & Meidani, ; Oh, Kim, Kim, Park, and Adeli, ; Rafiei & Adeli, , ; Rafiei, Khushefati, Demirboga, & Adeli, ; Yeum, Dyke, Ramirez, & Benes, ). Many of these studies applied DL in computer vision‐based Structural Health Monitoring (SHM).…”

Section: Introductionmentioning

confidence: 99%

“…The rapid development of Machine Learning (ML) and Deep Learning (DL) technologies and great improvement of Graphics Processing Unit (GPU) hardware motivate further developments and applications toward Artificial Intelligence (AI) and Intelligence Augmentation (IA) research area in many fields of science and engineering. Even in the traditional Civil and Structural Engineering areas, increased number of studies related to ML and DL have been taking place and resulting in promising findings (Kang & Cha, 2018;Lin, Nie, & Ma, 2017;Maeda, Sekimoto, Seto, Kashiyama, & Omata, 2018;Nabian & Meidani, 2018;Oh, Kim, Kim, Park, and series of experiments on detecting structural component type and damage severity and type on structural object level based on a small data set, where Transfer Learning (TL) and online Affine Data Augmentation (ADA) were applied. These two methods, that is, TL and ADA, effectively alleviated the negative influence (namely, overfitting) of labeled data deficiency.…”

Section: Introductionmentioning

confidence: 99%

Deep leaf‐bootstrapping generative adversarial network for structural image data augmentation

Gao

Kong

Mosalam

2019

Computer aided Civil Eng

111

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Employing Deep Learning (DL) technologies to solve Civil Engineering problems is an emerging topic in recent years. However, due to the lack of labeled data, it is difficult to obtain accurate results with DL. One commonly used method to tackle this issue is to use affine transformation to augment the data set, but it can only generate new images that are highly correlated with the original ones. Moreover, unlike normal natural objects, distribution of structural images is much more complex and mixed. To address these challenges, Generative Adversarial Network (GAN) can be one feasible choice. We introduce one specific generative model, namely, Deep Convolutional Generative Adversarial Network (DCGAN) and propose a Leaf‐Bootstrapping (LB) method to improve the performance of this DCGAN. To effectively and quantitatively evaluate the quality of the synthetic images generated by DCGAN to complement human evaluation, Self‐Inception Score (SIS) and Generalization Ability (GA) are proposed. We also propose a pipeline based on Transfer Learning (TL) using synthetic images to help enhance a weak classifier performance under the condition of low‐data regime and limited computational resources. Finally, we conduct computer experiments with the proposed methods for two scenarios (scene level identification and damage state check) and one special synthetic data aggregation case. The results demonstrate the effectiveness and robustness of the proposed methods.

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“…Therefore, a more carefully configured deep neural network (DNN) is essential for solving the true variety of real‐world problems. For example, different DNNs have been proposed to identify cracks in images with disturbing background interference (F. C. Chen & Jahanshahi, ; Kang & Cha, ; R. Li, Yuan, Zhang, & Yuan, ; Liang, ; Y. Xu, Bao et al., ). In addition, the noising issues and the layer multiplexing scheme in DNNs have been discussed (Koziarski & Cyganek, ; Ortega‐Zamorano, Jerez, Gómez, & Franco, ).…”

Section: Introductionmentioning

confidence: 99%

Concrete crack detection with handwriting script interferences using faster region‐based convolutional neural network

Deng

Lee

2019

Computer aided Civil Eng

168

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The current bridge maintenance practice generally involves manual visual inspection, which is highly subjective and unreliable. A technique that can automatically detect defects, for example, surface cracks, is essential so that early warnings can be triggered to prevent disaster due to structural failure. In this study, to permit automatic identification of concrete cracks, an ad‐hoc faster region‐based convolutional neural network (faster R‐CNN) was applied to contaminated real‐world images taken from concrete bridges with complex backgrounds, including handwriting. A dataset of 5,009 cropped images was generated and labeled for two different objects, cracks and handwriting. The proposed network was then trained and tested using the generated image dataset. Four full‐scale images that contained complex disturbance information were used to assess the performance of the trained network. The results of this study demonstrate that faster R‐CNN can automatically locate crack from raw images, even with the presence of handwriting scripts. For comparative study, the proposed network is also compared with You Only Look Once v2 detection technique.

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Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging

Cited by 295 publications

References 38 publications

Automatic classification of defective photovoltaic module cells in electroluminescence images

Automatic classification of defective photovoltaic module cells in electroluminescence images

Deep leaf‐bootstrapping generative adversarial network for structural image data augmentation

Concrete crack detection with handwriting script interferences using faster region‐based convolutional neural network

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