scholarly journals The Algorithm of Concrete Surface Crack Detection Based on the Genetic Programming and Percolation Model

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 57592-57603 ◽  
Author(s):  
Zhong Qu ◽  
Yu-Xiang Chen ◽  
Ling Liu ◽  
Yi Xie ◽  
Qiang Zhou
Keyword(s):  
Genetic Programming ◽  
Surface Crack ◽  
Crack Detection ◽  
Concrete Surface ◽  
Percolation Model

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.

scholarly journals Concrete surface crack detection with the improved pre-extraction and the second percolation processing methods

PLoS ONE ◽  
2018 ◽  
Vol 13 (7) ◽  
pp. e0201109 ◽  
Author(s):  
Zhong Qu ◽  
Fang-Rong Ju ◽  
Yang Guo ◽  
Ling Bai ◽  
Kuo Chen
Keyword(s):  
Surface Crack ◽  
Crack Detection ◽  
Concrete Surface ◽  
Processing Methods

scholarly journals A Hybrid Image Enhancement Algorithm for Effective Concrete Surface Crack Classification

2021 ◽  
Vol 23 (09) ◽  
pp. 1283-1297
Author(s):  
Sheerin Sitara Noor Mohamed ◽  
◽  
Kavitha Srinivasan ◽  
Keyword(s):  
Image Enhancement ◽  
Surface Crack ◽  
Crack Detection ◽  
Wiener Filter ◽  
Concrete Surface ◽  
Morphological Operations ◽  
Low Contrast ◽  
Crack Classification ◽  
Value Decomposition ◽  
Surface Images

Huge number of images are acquired and analysed every day for a range of applications in civil infrastructure. One such application is the identification of cracks in concrete surface images, which is a challenge owing to their low contrast and resolution, blurriness, noise and information loss. Existing image enhancement algorithms improve either contrast or resolution to a rather limited extent. This paper proposes a Hybrid Image Enhancement (HIE) algorithm to improve both the contrast and resolution of concrete surface images using the Wavelet transform and Singular Value Decomposition (SVM). The enhanced concrete surface crack images are classified into specific crack types. The classification comprises preprocessing, crack detection, feature extraction and crack classification. The images are initially preprocessed using the Wiener filter to remove blurriness, following which cracks are detected using morphological operations and discontinuities in the segmented crack regions eliminated using the K-Dimensional Tree algorithm. Features are extracted from the segmented regions using statistical and geometric features. The image is classified thereafter into specific crack types using algorithms from three different neural network, kernel and tree based categories. The proposed HIE algorithm is validated using quantitative metrics and the results obtained are compared with those from State-of-the-Art methods and datasets. The results have shown that the HIE algorithm offers significantly improved accuracy of between 6% and 10% in the classification of concrete surface images.

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.

Sign in / Sign up

Export Citation Format

Share Document