Supervised Deep Restricted Boltzmann Machine for Estimation of Concrete

604

338

Research on damage detection of road surfaces using image processing techniques has been actively conducted. This study makes three contributions to address road damage detection issues. First, to the best of our knowledge, for the first time, a large‐scale road damage data set is prepared, comprising 9,053 road damage images captured using a smartphone installed on a car, with 15,435 instances of road surface damage included in these road images. Next, we used state‐of‐the‐art object detection methods using convolutional neural networks to train the damage detection model with our data set, and compared the accuracy and runtime speed on both, using a GPU server and a smartphone. Finally, we demonstrate that the type of damage can be classified into eight types with high accuracy by applying the proposed object detection method. The road damage data set, our experimental results, and the developed smartphone application used in this study are publicly available (https://github.com/sekilab/RoadDamageDetector/).

Section: Related Workmentioning

confidence: 99%

Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images

Maeda

Sekimoto

Seto

et al. 2018

604

338

“…In general, the implementation of these methods involves the following three steps: (1) contrast enhancement, (2) mathematical morphological processing, and (3) information extraction using linear filters. Other machine learning methods, such as artificial neural networks (ANN) (Adeli and Yeh, ; Eldin and Senouci, ; Jin and Zhou, ), support vector machine (SVM) (Qu et al., ), Adaboost (Cord and Chambon, ), K‐nearest neighbors algorithm (Lei and Zuo, ), grouping techniques (Yeum and Dyke, ) and Restricted Boltzmann Machine (Rafiei and Adeli, , ; Rafiei et al., ) have also been used in the field of civil engineering for crack or damage detection and achieved some good results. However, a common problem with these methods is the inability to handle complex background images.…”

Section: Introductionmentioning

confidence: 99%

A Fast Detection Method via Region‐Based Fully Convolutional Neural Networks for Shield Tunnel Lining Defects

Xue

2018

271

142

Tunnel lining defects are an important indicator reflecting the safety status of shield tunnels. Inspired by the state‐of‐the‐art deep learning, a method for automatic intelligent classification and detection methodology of tunnel lining defects is presented. A fully convolutional network (FCN) model for classification is proposed. Information about defects, collected using charge‐coupled device cameras, was used to train the model. The model's performance was compared to those of GoogLeNet and VGG. The best‐set accuracy of the proposed model was over 95% at a test‐time speed of 48 ms per image. For defects detection, image features were computed from large‐scale images by the FCN and then detected using a region proposal network and position‐sensitive region of interest pooling. Some indices (detection rate, detection accuracy, and detection efficiency, locating accuracy) were used to evaluate the model. The comparisons with faster R‐CNN and a traditional method were conducted. The results show that the model is very fast and efficient, allowing automatic intelligent classification and detection of tunnel lining defects.

“…Unlike a standard artificial neural network, it does not require definition of specific damage‐sensitive features. Interest in the SHM discipline has been increasing in the application of the powerful CNN approach (Soukup and Huber‐Mörk, ; Lin et al., ; Rafiei et al., ). And other recent engineering applications of deep learning have been researched for SHM (Koziarski and Cyganek, ; Ortega‐Zamorano et al., ; Rafiei and Adeli, ).…”

Section: Introductionmentioning

confidence: 99%

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

Kang

Cha

2018

283

144

Visual inspection has traditionally been used for structural health monitoring. However, assessments conducted by trained inspectors or using contact sensors on structures for monitoring are costly and inefficient because of the number of inspectors and sensors required. To date, data acquisition using unmanned aerial vehicles (UAVs) equipped with cameras has become popular, but UAVs require skilled pilots or a global positioning system (GPS) for autonomous flight. Unfortunately, GPS cannot be used by a UAV for autonomous flight near some parts of certain structures (e.g., beneath a bridge), but these are the critical locations that should be inspected to monitor and maintain structural health. To address this difficulty, this article proposes an autonomous UAV method using ultrasonic beacons to replace the role of GPS, a deep convolutional neural network (CNN) for damage detection, and a geo‐tagging method for the localization of damage. Concrete cracks, as an example of structural damage, were successfully detected with 97.7% specificity and 91.9% sensitivity, by processing video data collected from an autonomous UAV.