Crack Detection Method of Sleeper Based on Cascade Convolutional Neural Network

This work presents a new method for sleeper crack identification based on cascade convolutional neural network (CNN) to address the problem of low efficiency and poor accuracy in the traditional detection method of sleeper crack identification. The proposed algorithm mainly includes improved You Only Look Once version 3 (YOLOv3) and the crack recognition network, where the crack recognition network includes two modules, the crack encoder-decoder network (CEDNet) and the crack residual refinement network (CRRNet). The improved YOLOv3 network is used to identify and locate cracks on sleepers and segment them after the sleeper on the ballast bed is extracted by using the gray projection method. The sleeper is inputted into CEDNet for crack feature extraction to predict the coarse crack saliency map. The prediction graph is inputted into CRRNet to improve its edge information and local region to achieve optimization. The accuracy of the crack identification model is improved by using a mixed loss function of binary cross-entropy (BCE), structural similarity index measure (SSIM), and intersection over union (IOU). Results show that this method can accurately detect the sleeper crack image. During object detection, the proposed method is compared with YOLOv3 in terms of directly locating sleeper cracks. It has an accuracy of 96.3%, a recall rate of 91.2%, a mean average precision (mAP) of 91.5%, and frames per second (FPS) of 76.6/s. In the crack extraction part, the F-weighted is 0.831, mean absolute error (MAE) is 0.0157, and area under the curve (AUC) is 0.9453. The proposed method has better recognition, higher efficiency, and robustness compared with the other network models.

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Crack Identification of Infrared Thermal Imaging Steel Sheet Based on Convolutional Neural Network

MATEC Web of Conferences ◽

10.1051/matecconf/201823201053 ◽

2018 ◽

Vol 232 ◽

pp. 01053

Author(s):

Wei Wang ◽

Qing Li

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Crack Detection ◽

Detection Efficiency ◽

Heat Transfer Process ◽

Crack Identification ◽

Thermal Excitation ◽

Thermal Imager ◽

Infrared Thermal Imaging ◽

Low Efficiency

Aiming at the low efficiency and poor anti-interference ability of traditional non-destructive testing technology in steel plate crack detection, a crack recognition method based on convolutional neural network for infrared thermal imager is proposed. Firstly, a rolling electric heating rod is developed as a thermal excitation source, and a new excitation method was used to thermally excite the surface to be inspected. Then, according to the principle of abnormal temperature generated during the heat transfer process, the temperature of the detected surface is analyzed. It is concluded that the temperature gradient on both sides of the crack is always the largest. Finally, the infrared thermal image after thermal excitation is collected as a training sample, and a convolutional neural network is built to train the sample. Experiments show that the convolutional neural network model can accurately identify the infrared image cracks. The detection efficiency is high and the robustness is strong. And the recognition accuracy on the test set reaches 96.82%.

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Crack Grid Detection and Calculation Based on Convolutional Neural Network

Canadian Journal of Civil Engineering ◽

10.1139/cjce-2020-0246 ◽

2021 ◽

Author(s):

Liyang Xiao ◽

Wei Li ◽

Ju Huyan ◽

Zhaoyun Sun ◽

Susan Tighe

Keyword(s):

Neural Network ◽

Image Quality ◽

Convolutional Neural Network ◽

Image Denoising ◽

Crack Detection ◽

Experimental Results ◽

Crack Identification ◽

Threshold Segmentation ◽

Improve Image Quality

This paper aims to develop a method of crack grid detection based on convolutional neural network. First, an image denoising operation is conducted to improve image quality. Next, the processed images are divided into grids of different, and each grid is fed into a convolutional neural network for detection. The pieces of the grids with cracks are marked and then returned to the original images. Finally, on the basis of the detection results, threshold segmentation is performed only on the marked grids. Information about the crack parameters is obtained via pixel scanning and calculation, which realises complete crack detection. The experimental results show that 30×30 grids perform the best with the accuracy value of 97.33%. The advantage of automatic crack grid detection is that it can avoid fracture phenomenon in crack identification and ensure the integrity of cracks.

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Automatic Tunnel Crack Detection Based on U-Net and a Convolutional Neural Network with Alternately Updated Clique

Sensors ◽

10.3390/s20030717 ◽

2020 ◽

Vol 20 (3) ◽

pp. 717 ◽

Cited By ~ 1

Author(s):

Gang Li ◽

Biao Ma ◽

Shuanhai He ◽

Xueli Ren ◽

Qiangwei Liu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Crack Width ◽

Crack Detection ◽

Detection Method ◽

Learning Algorithm ◽

Semantic Segmentation ◽

Fully Convolutional Networks ◽

Deep Learning Algorithm ◽

Mean Width

Regular crack inspection of tunnels is essential to guarantee their safe operation. At present, the manual detection method is time-consuming, subjective and even dangerous, while the automatic detection method is relatively inaccurate. Detecting tunnel cracks is a challenging task since cracks are tiny, and there are many noise patterns in the tunnel images. This study proposes a deep learning algorithm based on U-Net and a convolutional neural network with alternately updated clique (CliqueNet), called U-CliqueNet, to separate cracks from background in the tunnel images. A consumer-grade DSC-WX700 camera (SONY, Wuxi, China) was used to collect 200 original images, then cracks are manually marked and divided into sub-images with a resolution of 496 × 496 pixels. A total of 60,000 sub-images were obtained in the dataset of tunnel cracks, among which 50,000 were used for training and 10,000 were used for testing. The proposed framework conducted training and testing on this dataset, the mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and F1-score are 92.25%, 86.96%, 86.32% and 83.40%, respectively. We compared the U-CliqueNet with fully convolutional networks (FCN), U-net, Encoder–decoder network (SegNet) and the multi-scale fusion crack detection (MFCD) algorithm using hypothesis testing, and it’s proved that the MIoU predicted by U-CliqueNet was significantly higher than that of the other four algorithms. The area, length and mean width of cracks can be calculated, and the relative error between the detected mean crack width and the actual mean crack width ranges from −11.20% to 18.57%. The results show that this framework can be used for fast and accurate crack semantic segmentation of tunnel images.

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Pavement Crack Detection and Segmentation Method Based on Improved Deep Learning Fusion Model

Mathematical Problems in Engineering ◽

10.1155/2020/8515213 ◽

2020 ◽

Vol 2020 ◽

pp. 1-22

Author(s):

Xiaoran Feng ◽

Liyang Xiao ◽

Wei Li ◽

Lili Pei ◽

Zhaoyun Sun ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Geometric Parameter ◽

Crack Detection ◽

Crack Identification ◽

Single Shot ◽

Road Maintenance ◽

Fusion Model ◽

Detection Model ◽

Crack Classification

Pavement damage is the main factor affecting road performance. Pavement cracking, a common type of road damage, is a key challenge in road maintenance. In order to achieve an accurate crack classification, segmentation, and geometric parameter calculation, this paper proposes a method based on a deep convolutional neural network fusion model for pavement crack identification, which combines the advantages of the multitarget single-shot multibox detector (SSD) convolutional neural network model and the U-Net model. First, the crack classification and detection model is applied to classify the cracks and obtain the detection confidence. Next, the crack segmentation network is applied to accurately segment the pavement cracks. By improving the feature extraction structure and optimizing the hyperparameters of the model, pavement crack classification and segmentation accuracy were improved. Finally, the length and width (for linear cracks) and the area (for alligator cracks) are calculated according to the segmentation results. Test results show that the recognition accuracy of the pavement crack identification method for transverse, longitudinal, and alligator cracks is 86.8%, 87.6%, and 85.5%, respectively. It is demonstrated that the proposed method can provide the category information for pavement cracks as well as the accurate positioning and geometric parameter information, which can be used directly for evaluating the pavement condition.

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Vision-based automated crack detection using convolutional neural networks for condition assessment of infrastructure

Structural Health Monitoring ◽

10.1177/1475921720965445 ◽

2020 ◽

pp. 147592172096544

Author(s):

Aravinda S Rao ◽

Tuan Nguyen ◽

Marimuthu Palaniswami ◽

Tuan Ngo

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Crack Detection ◽

Concrete Structures ◽

Area Under The Curve ◽

Network Models ◽

Condition Assessment ◽

Neural Network Models ◽

Data Set ◽

Inspection Method

With the growing number of aging infrastructure across the world, there is a high demand for a more effective inspection method to assess its conditions. Routine assessment of structural conditions is a necessity to ensure the safety and operation of critical infrastructure. However, the current practice to detect structural damages, such as cracks, depends on human visual observation methods, which are prone to efficiency, cost, and safety concerns. In this article, we present an automated detection method, which is based on convolutional neural network models and a non-overlapping window-based approach, to detect crack/non-crack conditions of concrete structures from images. To this end, we construct a data set of crack/non-crack concrete structures, comprising 32,704 training patches, 2074 validation patches, and 6032 test patches. We evaluate the performance of our approach using 15 state-of-the-art convolutional neural network models in terms of number of parameters required to train the models, area under the curve, and inference time. Our approach provides over 95% accuracy and over 87% precision in detecting the cracks for most of the convolutional neural network models. We also show that our approach outperforms existing models in literature in terms of accuracy and inference time. The best performance in terms of area under the curve was achieved by visual geometry group-16 model (area under the curve = 0.9805) and best inference time was provided by AlexNet (0.32 s per image in size of 256 × 256 × 3). Our evaluation shows that deeper convolutional neural network models have higher detection accuracies; however, they also require more parameters and have higher inference time. We believe that this study would act as a benchmark for real-time, automated crack detection for condition assessment of infrastructure.

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Instant bridge visual inspection using an unmanned aerial vehicle by image capturing and geo-tagging system and deep convolutional neural network

Structural Health Monitoring ◽

10.1177/1475921720932384 ◽

2020 ◽

pp. 147592172093238

Author(s):

Muhammad Rakeh Saleem ◽

Jong-Woong Park ◽

Jin-Hwan Lee ◽

Hyung-Jo Jung ◽

Muhammad Zohaib Sarwar

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Visual Inspection ◽

Crack Detection ◽

Damage Identification ◽

Deep Convolutional Neural Network ◽

Crack Identification ◽

Image Capturing ◽

Damage Map ◽

Tagging System

The structural condition of bridges is generally assessed using manual visual inspection. However, this approach consumes labor, time, and capital, and produces subjective results. Therefore, industries today are using automated visual inspection approaches, which quantify and localize damages such as cracks using robots and computer vision. This paper proposes an instant damage identification and localization approach that uses an image capturing and geo-tagging system and deep convolutional neural network for crack detection. The image capturing and geo-tagging allows the geo-tagging of three-dimensional coordinates and camera pose data with bridge inspection images; the deep convolutional neural network is trained for automated crack identification. The damages extracted by the convolutional neural network are instantly transformed into a global bridge damage map, with georeferencing data acquired using the image capturing and geo-tagging. This method is experimentally validated through a lab-scale test on a wall and a field test on a bridge to demonstrate the performance of the instant damage map.

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Image-Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique

Advances in Civil Engineering ◽

10.1155/2019/6520620 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 15

Author(s):

Shengyuan Li ◽

Xuefeng Zhao

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Crack Detection ◽

Detection Method ◽

Smartphone Application ◽

Concrete Surface ◽

Search Technique ◽

Concrete Crack ◽

Learning Rates ◽

Complex Image

Crack detection is important for the inspection and evaluation during the maintenance of concrete structures. However, conventional image-based methods need extract crack features using complex image preprocessing techniques, so it can lead to challenges when concrete surface contains various types of noise due to extensively varying real-world situations such as thin cracks, rough surface, shadows, etc. To overcome these challenges, this paper proposes an image-based crack detection method using a deep convolutional neural network (CNN). A CNN is designed through modifying AlexNet and then trained and validated using a built database with 60000 images. Through comparing validation accuracy under different base learning rates, 0.01 was chosen as the best base learning rate with the highest validation accuracy of 99.06%, and its training result is used in the following testing process. The robustness and adaptability of the trained CNN are tested on 205 images with 3120 × 4160 pixel resolutions which were not used for training and validation. The trained CNN is integrated into a smartphone application to mobile more public to detect cracks in practice. The results confirm that the proposed method can indeed detect cracks in images from real concrete surfaces.

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Convolutional Neural network based Online Rail surface Crack Detection

2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS) ◽

10.1109/iciccs51141.2021.9432080 ◽

2021 ◽

Author(s):

Chitturi Akhila ◽

Cheemala Astha Diamond ◽

A. Mary Posonia

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Surface Crack ◽

Crack Detection

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End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

BMJ Innovations ◽

10.1136/bmjinnov-2021-000668 ◽

2021 ◽

Vol 7 (2) ◽

pp. 356-362

Author(s):

Harry Coppock ◽

Alex Gaskell ◽

Panagiotis Tzirakis ◽

Alice Baird ◽

Lyn Jones ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Low Cost ◽

Area Under The Curve ◽

Alternative Form ◽

Economic Damage ◽

Audio Signals ◽

Operating Characteristics ◽

Data Set ◽

Empirical Performance

BackgroundSince the emergence of COVID-19 in December 2019, multidisciplinary research teams have wrestled with how best to control the pandemic in light of its considerable physical, psychological and economic damage. Mass testing has been advocated as a potential remedy; however, mass testing using physical tests is a costly and hard-to-scale solution.MethodsThis study demonstrates the feasibility of an alternative form of COVID-19 detection, harnessing digital technology through the use of audio biomarkers and deep learning. Specifically, we show that a deep neural network based model can be trained to detect symptomatic and asymptomatic COVID-19 cases using breath and cough audio recordings.ResultsOur model, a custom convolutional neural network, demonstrates strong empirical performance on a data set consisting of 355 crowdsourced participants, achieving an area under the curve of the receiver operating characteristics of 0.846 on the task of COVID-19 classification.ConclusionThis study offers a proof of concept for diagnosing COVID-19 using cough and breath audio signals and motivates a comprehensive follow-up research study on a wider data sample, given the evident advantages of a low-cost, highly scalable digital COVID-19 diagnostic tool.

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