Pavement Crack Detection Algorithm Based on Image Processing Analysis

2016 ◽  
Author(s):  
Zhu Qingbo
Keyword(s):  
Image Processing ◽  
Crack Detection ◽  
Detection Algorithm ◽  
Pavement Crack Detection

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.

A Novel Pavement Crack Detection Algorithm Using Interlaced Low-Rank Group Convolution Hybrid Deep Network Under a Complex Background

2020 ◽  
Vol 57 (14) ◽  
pp. 141031
Author(s):  
李刚 Li Gang ◽  
刘强伟 Liu Qiangwei ◽  
万健 Wan Jian ◽  
马彪 Ma Biao ◽  
李莹 Li Ying
Keyword(s):  
Crack Detection ◽  
Detection Algorithm ◽  
Low Rank ◽  
Deep Network ◽  
Complex Background ◽  
Pavement Crack Detection ◽  
Rank Group

scholarly journals Research on Crack Detection Algorithm of the Concrete Bridge Based on Image Processing

2019 ◽  
Vol 154 ◽  
pp. 610-616 ◽  
Author(s):  
Yun Wang ◽  
Ju Yong Zhang ◽  
Jing Xin Liu ◽  
Yin Zhang ◽  
Zhi Ping Chen ◽  
...  
Keyword(s):  
Image Processing ◽  
Crack Detection ◽  
Detection Algorithm ◽  
Concrete Bridge

scholarly journals Automatic Road Pavement Assessment with Image Processing: Review and Comparison

2011 ◽  
Vol 2011 ◽  
pp. 1-20 ◽  
Author(s):  
Sylvie Chambon ◽  
Jean-Marc Moliard
Keyword(s):  
Image Processing ◽  
Crack Detection ◽  
State Of The Art ◽  
Civil Infrastructures ◽  
The Road ◽  
Multi Scale ◽  
Road Pavement ◽  
Optical Images ◽  
Detection Approach ◽  
Pavement Crack Detection

In the field of noninvasive sensing techniques for civil infrastructures monitoring, this paper addresses the problem of crack detection, in the surface of the French national roads, by automatic analysis of optical images. The first contribution is a state of the art of the image-processing tools applied to civil engineering. The second contribution is about fine-defect detection in pavement surface. The approach is based on a multi-scale extraction and a Markovian segmentation. Third, an evaluation and comparison protocol which has been designed for evaluating this difficult task—the road pavement crack detection—is introduced. Finally, the proposed method is validated, analysed, and compared to a detection approach based on morphological tools.

scholarly journals Concrete Crack Detection Algorithm Based on Deep Residual Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xiuying Meng
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Deep Learning ◽  
Crack Detection ◽  
Learning Algorithm ◽  
Detection Algorithm ◽  
Concrete Pavement ◽  
Computer Hardware ◽  
Detection Accuracy ◽  
Generalization Ability

Crack is the early expression form of the concrete pavement disease. Early discovery and treatment of it can play an important role in the maintenance of the pavement. With ongoing advancements in computer hardware technology, continual optimization of deep learning algorithms, as compared to standard digital image processing algorithms, utilizing automation of crack detection technology has a deep learning algorithm that is more exact. As a result of the benefits of greater robustness, the study of concrete pavement crack picture has become popular. In view of the poor effect and weak generalization ability of traditional image processing technology on image segmentation of concrete cracks, this paper studies the image segmentation algorithm of concrete cracks based on convolutional neural network and designs an end-to-end segmentation model based on ResNet101. It integrates more low-level features, which make the fracture segmentation results more refined and closer to the practical application scenarios. Compared with other methods, the algorithm in this paper has achieved higher detection accuracy and generalization ability.

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