scholarly journals Induction Thermography for Surface Crack Detection and Depth Determination

2018 ◽  
Vol 8 (2) ◽  
pp. 257 ◽  
Author(s):  
Beate Oswald-Tranta
Keyword(s):  
Surface Crack ◽  
Crack Detection ◽  
Depth Determination

scholarly journals CNN-Based Road-Surface Crack Detection Model That Responds to Brightness Changes

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1402
Author(s):  
Taehee Lee ◽  
Yeohwan Yoon ◽  
Chanjun Chun ◽  
Seungki Ryu
Keyword(s):  
Surface Crack ◽  
Autonomous Vehicles ◽  
Crack Detection ◽  
Model Performance ◽  
Semantic Segmentation ◽  
Road Surface ◽  
Image Brightness ◽  
The Road ◽  
Detection Model ◽  
Artificial Intelligence Models

Poor road-surface conditions pose a significant safety risk to vehicle operation, especially in the case of autonomous vehicles. Hence, maintenance of road surfaces will become even more important in the future. With the development of deep learning-based computer image processing technology, artificial intelligence models that evaluate road conditions are being actively researched. However, as the lighting conditions of the road surface vary depending on the weather, the model performance may degrade for an image whose brightness falls outside the range of the learned image, even for the same road. In this study, a semantic segmentation model with an autoencoder structure was developed for detecting road surface along with a CNN-based image preprocessing model. This setup ensures better road-surface crack detection by adjusting the image brightness before it is input into the road-crack detection model. When the preprocessing model was applied, the road-crack segmentation model exhibited consistent performance even under varying brightness values.

scholarly journals Cement Pavement Surface Crack Detection Based on Image Processing

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Hongwei Lei ◽  
Jianlian Cheng ◽  
Qi Xu
Keyword(s):  
Image Processing ◽  
Laplace Operator ◽  
Image Recognition ◽  
Surface Crack ◽  
Iteration Method ◽  
Crack Detection ◽  
Binary Image ◽  
Threshold Method ◽  
Otsu Method ◽  
Pavement Crack Detection

This article introduces the application of image recognition technology in cement pavement crack detection and put forward to method for determining threshold about grayscale stretching. the algorithm is designed about binarization which has a self-adaptive characteristic. After the image is preprocessed, we apply 2D Wavelet and Laplace operator to process the image. According to the characteristic of pixel of gray image, an algorithm designed on binarization for Binary image. The feasibility of this method can be verified the image processed by comparing with the results of three algorithms: Otsu method, iteration method and fixed threshold method.

GAMMAFLUX: A SYSTEM FOR AUTOMATED SURFACE CRACK DETECTION IN MAGNETIC PARTICLE INSPECTION

1989 ◽  
pp. 667-669
Author(s):  
R. Link ◽  
H.P. Busse ◽  
C. Stapf ◽  
G. Streckenbach ◽  
H. Wiacker ◽  
...  
Keyword(s):  
Surface Crack ◽  
Crack Detection ◽  
Magnetic Particle ◽  
Magnetic Particle Inspection

A vision-based active learning convolutional neural network model for concrete surface crack detection

2020 ◽  
Vol 23 (13) ◽  
pp. 2952-2964
Author(s):  
Zhen Wang ◽  
Guoshan Xu ◽  
Yong Ding ◽  
Bin Wu ◽  
Guoyu Lu
Keyword(s):  
Neural Network ◽  
Active Learning ◽  
Convolutional Neural Network ◽  
Surface Crack ◽  
Crack Detection ◽  
Concrete Surface ◽  
Learning Ability ◽  
Training Method ◽  
And Training ◽  
New Framework

Concrete surface crack detection based on computer vision, specifically via a convolutional neural network, has drawn increasing attention for replacing manual visual inspection of bridges and buildings. This article proposes a new framework for this task and a sampling and training method based on active learning to treat class imbalances. In particular, the new framework includes a clear definition of two categories of samples, a relevant sliding window technique, data augmentation and annotation methods. The advantages of this framework are that data integrity can be ensured and a very large amount of annotation work can be saved. Training datasets generated with the proposed sampling and training method not only are representative of the original dataset but also highlight samples that are highly complex, yet informative. Based on the proposed framework and sampling and training strategy, AlexNet is re-tuned, validated, tested and compared with an existing network. The investigation revealed outstanding performances of the proposed framework in terms of the detection accuracy, precision and F1 measure due to its nonlinear learning ability, training dataset integrity and active learning strategy.

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