CIVILICA We Respect the Science
Publisher of Iranian Journals and Conference Proceedings
Paper
title

Automatic road crack detection and classification using image processing techniques, machine learning and integrated models in urban areas: A novel image binarization technique

Credit to Download: 1 | Page Numbers 13 | Abstract Views: 103
Year: 2017
COI code: IIEC14_033
Paper Language: English

How to Download This Paper

For Downloading the Fulltext of CIVILICA papers please visit the orginal Persian Section of website.

Authors Automatic road crack detection and classification using image processing techniques, machine learning and integrated models in urban areas: A novel image binarization technique

  Abbas Ahmadi - Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
  Sadjad Khalesi - Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
  MohammadReza Bagheri - Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran

Abstract:

The quality of the road pavement has always been one of the major concerns for governments around the world. Cracks in the asphalt are one of the most common road tensions that generally threaten the safety of roads and highways. In recent years, automated inspection methods such as image and video processing have been considered due to the high cost and error of manual methods. For this purpose, different image processing techniques and classification methods have been developed by many researchers. In this study, we propose an integrated model includes a heuristic image segmentation technique for crack detection. Furthermore, the accuracy of various classification models such as KNN, decision tree and SVM will be compared. Finally, 5-fold cross validation shows that Subspace KNN method will be more accurate than other classification models which is used in this study. On the other hand, we also simulate the depth and density of different segment of crack by utilizing density matrix values.

Keywords:

crack detection, classification, machine learning, integrated model, segmentation

Perma Link

https://www.civilica.com/Paper-IIEC14-IIEC14_033.html
COI code: IIEC14_033

how to cite to this paper:

If you want to refer to this article in your research, you can easily use the following in the resources and references section:
Ahmadi, Abbas; Sadjad Khalesi & MohammadReza Bagheri, 2017, Automatic road crack detection and classification using image processing techniques, machine learning and integrated models in urban areas: A novel image binarization technique, 14th International Industrial Engineering Conference, تهران, انجمن مهندسي صنايع ايران - دانشگاه علم و صنعت ايران, https://www.civilica.com/Paper-IIEC14-IIEC14_033.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Ahmadi, Abbas; Sadjad Khalesi & MohammadReza Bagheri, 2017)
Second and more: (Ahmadi; Khalesi & Bagheri, 2017)
For a complete overview of how to citation please review the following CIVILICA Guide (Citation)

Scientometrics

The University/Research Center Information:
Type: state university
Paper No.: 19662
in University Ranking and Scientometrics the Iranian universities and research centers are evaluated based on scientific papers.

Research Info Management

Export Citation info of this paper to research management softwares

New Related Papers

Iran Scientific Advertisment Netword

Share this paper

WHAT IS COI?

COI is a national code dedicated to all Iranian Conference and Journal Papers. the COI of each paper can be verified online.