Crack identification inside on-site steel box girder based on fusion convolutional neural network

2018 ◽  
Author(s):  
Yang Xu ◽  
Hui Li ◽  
Jiahui Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Identification ◽  
Box Girder ◽  
Steel Box Girder

Crack Grid Detection and Calculation Based on Convolutional Neural Network

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.

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

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.

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

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.

Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images

2018 ◽  
Vol 18 (3) ◽  
pp. 653-674 ◽  
Author(s):  
Yang Xu ◽  
Yuequan Bao ◽  
Jiahui Chen ◽  
Wangmeng Zuo ◽  
Hui Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Structure ◽  
Network Architecture ◽  
Confusion Matrix ◽  
Super Resolution ◽  
Identification Accuracy ◽  
Crack Identification ◽  
Threshold Method ◽  
Neural Network Structure

This study conducts crack identification from real-world images containing complicated disturbance information (cracks, handwriting scripts, and background) inside steel box girders of bridges. Considering the multilevel and multi-scale features of the input images, a modified fusion convolutional neural network architecture is proposed. As input, 350 raw images are taken with a consumer-grade camera and divided into sub-images with resolution of 64 × 64 pixels (67,200 in total). A regular convolutional neural network structure is employed as baseline to demonstrate the accuracy benefits from the proposed fusion convolutional neural network structure. The confusion matrix is defined for prediction performance evaluation on the test set. A total of six additional entire raw images are used to investigate the robustness and feasibility of the proposed approach. A binary conversion process based on the optimal entropy threshold method is applied and closely followed to identify the crack pixels in the sub-images. The effect of the super-resolution inputs on accuracy is investigated. Results show that the trained modified fusion convolutional neural network can automatically detect the cracks, handwriting, and background from the raw images. The recognition errors of the fusion convolutional neural network in both the training and validation processes are smaller than those of the regular convolutional neural network. The super-resolution process hurts the general identification accuracy.

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

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%.

scholarly journals Crack Detection Method of Sleeper Based on Cascade Convolutional Neural Network

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Liming Li ◽  
Shubin Zheng ◽  
Chenxi Wang ◽  
Shuguang Zhao ◽  
Xiaodong Chai ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Detection ◽  
Detection Method ◽  
Similarity Index ◽  
Area Under The Curve ◽  
Saliency Map ◽  
Crack Identification ◽  
Crack Recognition ◽  
Low Efficiency

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.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network
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