Image recognition with deep neural networks in presence of noise – Dealing with and taking advantage of distortions

279

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.

Section: Discussionmentioning

confidence: 99%

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

Xue

2018

279

142

“…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, ). Moreover, the faster region‐based CNN (Faster R‐CNN) method (Ren et al., ) has been applied to the detection and localization of multiple damage types for a steel girder bridge (Cha et al., ).…”

Section: Introductionmentioning

confidence: 99%

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

Kang

Cha

2018

294

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.

“…For example, a robust automated image processing method based on multiple sequential image filtering was used to detect superficial cracks on concrete structures (Nishikawa, Yoshida, Sugiyama, & Fujino, 2012). However, these methods have some limits, such as manual feature extraction and sensitivity to noise (stains, shadows, and nonuniform lighting conditions) (Koziarski & Cyganek, 2017;Ortega-Zamorano, Jerez, Gómez, & Franco, 2017). Automatic crack detection and classification methods that leveraged complementary metal-oxide semiconductor industrial cameras were used in a machine vision application for defect detection (Zhang, Zhang, Qi, & Liu, 2014).…”

Section: Introductionmentioning

confidence: 99%

Autonomous damage segmentation and measurement of glazed tiles in historic buildings via deep learning

Wang

Zhao

Zou

et al. 2019

Detecting and measuring the damage on historic glazed tiles plays an important role in the maintenance and protection of historic buildings. However, the current visual inspection method for identifying and assessing superficial damage on historic buildings is time and labor intensive. In this article, a novel two‐level object detection, segmentation, and measurement strategy for large‐scale structures based on a deep‐learning technique is proposed. The data in this study are from the roof images of the Palace Museum in China. The first level of the model, which is based on the Faster region‐based convolutional neural network (Faster R‐CNN), automatically detects and crops two types of glazed tile photographs from 100 roof images (2,488 × 3,264 pixels). The average precision values (AP) for roll roofing and pan tiles are 0.910 and 0.890, respectively. The cropped images are used to form a dataset for training a Mask R‐CNN model. The second level of the model, which is based on Mask R‐CNN, automatically segments and measures the damage based on the cropped historic tile images; the AP for the damage segmentation is 0.975. Based on Mask R‐CNN, the predicted pixel‐level damage segmentation result is used to quantitatively measure the morphological features of the damage, such as the damage topology, area, and ratio. To verify the performance of the proposed method, a comparative study was conducted with Mask R‐CNN and a fully convolutional network. This is the first attempt at employing a two‐level strategy to automatically detect, segment, and measure large‐scale superficial damage on historic buildings based on deep learning, and it achieved good results.