Pavement Distress Detection Using Random Decision Forests

2015 ◽  
pp. 95-102 ◽  
Author(s):  
Limeng Cui ◽  
Zhiquan Qi ◽  
Zhensong Chen ◽  
Fan Meng ◽  
Yong Shi
Keyword(s):  
Pavement Distress ◽  
Random Decision Forests ◽  
Pavement Distress Detection

scholarly journals Pavement Distress Detection Methods: A Review

2018 ◽  
Vol 3 (4) ◽  
pp. 58 ◽  
Author(s):  
Antonella Ragnoli ◽  
Maria De Blasiis ◽  
Alessandro Di Benedetto
Keyword(s):  
Data Collection ◽  
Operating Cost ◽  
Pavement Management ◽  
Point Of View ◽  
Detection Methods ◽  
Technical Improvement ◽  
Pavement Distress ◽  
The Road ◽  
Road Pavement ◽  
Pavement Distress Detection

The road pavement conditions affect safety and comfort, traffic and travel times, vehicles operating cost, and emission levels. In order to optimize the road pavement management and guarantee satisfactory mobility conditions for all road users, the Pavement Management System (PMS) is an effective tool for the road manager. An effective PMS requires the availability of pavement distress data, the possibility of data maintenance and updating, in order to evaluate the best maintenance program. In the last decade, many researches have been focused on pavement distress detection, using a huge variety of technological solutions for both data collection and information extraction and qualification. This paper presents a literature review of data collection systems and processing approach aimed at the pavement condition evaluation. Both commercial solutions and research approaches have been included. The main goal is to draw a framework of the actual existing solutions, considering them from a different point of view in order to identify the most suitable for further research and technical improvement, while also considering the automated and semi-automated emerging technologies. An important attempt is to evaluate the aptness of the data collection and extraction to the type of distress, considering the distress detection, classification, and quantification phases of the procedure.

Vibration vs. vision: best approach for automated pavement distress detection

2020 ◽  
Vol 13 (4) ◽  
pp. 402-410 ◽  
Author(s):  
Janani Lekshmipathy ◽  
Nisha M. Samuel ◽  
Sunitha Velayudhan
Keyword(s):  
Pavement Distress ◽  
Pavement Distress Detection

Automatic pavement distress detection system

1998 ◽  
Vol 108 (1-4) ◽  
pp. 219-240 ◽  
Author(s):  
H.D. Cheng ◽  
M. Miyojim
Keyword(s):  
Detection System ◽  
Pavement Distress ◽  
Pavement Distress Detection

Autonomous pavement distress detection using ground penetrating radar and region-based deep learning

Measurement ◽  
2020 ◽  
Vol 164 ◽  
pp. 108077 ◽  
Author(s):  
Jie Gao ◽  
Dongdong Yuan ◽  
Zheng Tong ◽  
Jiangang Yang ◽  
Di Yu
Keyword(s):  
Deep Learning ◽  
Ground Penetrating Radar ◽  
Pavement Distress ◽  
Ground Penetrating ◽  
Pavement Distress Detection

scholarly journals Automated Pavement Distress Detection and Deterioration Analysis Using Street View Map

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 76163-76172
Author(s):  
Xu Lei ◽  
Chenglong Liu ◽  
Li Li ◽  
Guiping Wang
Keyword(s):  
Pavement Distress ◽  
Street View ◽  
Pavement Distress Detection
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