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Pavement Distress Detection Using Artificial Intelligence Algorithm
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Pavement distress detection and severity analysis
SPIE Proceedings, 2011Automatic recognition of road distresses has been an important research area since it reduces economic loses before cracks and potholes become too severe. Existing systems for automated pavement defect detection commonly require special devices such as lights, lasers, etc, which dramatically increase the cost and limit the system to certain ...
E. Salari, G. Bao
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An image-based pavement distress detection and classification
2012 IEEE International Conference on Electro/Information Technology, 2012This paper presents a pavement segmentation and crack detection system from pavement images with complicated background information. The proposed method consists of three steps. In the first step, a Support Vector Machine, which shows a high degree of accuracy in classifying data, was employed to classify the image into two categories: a pavement group
Ezzatollah Salari, Dingxin Ouyang
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Pavement Distress Detection Based on Transfer Learning
2018 5th International Conference on Systems and Informatics (ICSAI), 2018With the rapid development of highway construction in China, more and more attention has been paid to highway maintenance. The traditional manual detection and recognition methods cannot meet the needs of highway development, so the research of detection and recognition technology based on road image has become particularly important.
Mingxin Nie, Kun Wang
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Automatic pavement distress detection system
Information Sciences, 1998Abstract Statistics published by the Federal Highway Administration indicates that maintenance and rehabilitation of highway pavements in the United States requires over $17 billion a year. Conventional visual and manual pavement distress analysis approaches that the inspectors traverse the roads, stop and measure the distress objects when they are ...
Heng-Da Cheng, Mario Miyojim
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Pavement distress detection and classification using a Genetic Algorithm
2011 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2011Over the years, Automated Image Analysis Systems (AIAS) have been developed for pavement surface analysis and management. Pavement distress segmentation is a key issue throughout the entire process of analyses. In this paper, an adaptive approach for pavement distress segmentation based on Genetic Algorithms is proposed.
Ezzatollah Salari, X. Yu
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Pavement Distress Detection Based on Nonsubsampled Contourlet Transform
2008 International Conference on Computer Science and Software Engineering, 2008Automatic recognition of road distresses has been a hot topic since it reduces economic loses before cracks and potholes become too severe. However, weak information of road distress and computing complexity make it difficult to detect road distress effectively.
Changxia Ma, Chunxia Zhao, Yingkun Hou
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Pavement Crack Distress Detection Based on Image Analysis
2010 International Conference on Machine Vision and Human-machine Interface, 2010A detecting approach has been developed in view of the properties of cracks in the pavement image. Because of the uneven illumination, threshold causes difficulties in applications of pavement image segmentation. By analyzing the signal model, we can use bilinear interpolation to obtain the correction image based on the background subset which is ...
Lou Jing, Zang Aiqin
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Novel System for Automatic Pavement Distress Detection
Journal of Computing in Civil Engineering, 1998Statistics published by the Federal Highway Administration indicate that maintenance and rehabilitation of highway pavements in the United States requires an expenditure of over $17 billion a year. In conventional visual and manual pavement distress analysis approaches, inspectors traverse roads and stop and measure distress objects when they are found.
H. D. Cheng, M. Miyojim
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A Two-Stream Context-Aware ConvNet for Pavement Distress Detection
2020 43rd International Conference on Telecommunications and Signal Processing (TSP), 2020Convolutional neural networks (ConvNets) are widely used for pavement distress analysis tasks in which features are typically extracted from a smaller image (e.g. $224\times 224$) that has been cropped from an orthophoto. This paper introduces a ConvNet-based method to classify partitioned segments of orthophotos for pavement distress by incorporating ...
Roland Lõuk +2 more
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