Published in: 14th International Industrial Engineering Conference
COI code: IIEC14_033
Paper Language: English
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Authors Automatic road crack detection and classiﬁcation using image processing techniques, machine learning and integrated models in urban areas: A novel image binarization techniqueAbbas 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
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)
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The University/Research Center Information:
Type: state university
Paper No.: 19662
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