scholarly journals Ground penetrating radar for buried utilities detection and mapping: a review

2021 ◽  
Vol 2107 (1) ◽  
pp. 012056
Author(s):  
Hasimah Ali ◽  
Nurul Syahirah Mohd Ideris ◽  
A F Ahmad Zaidi ◽  
M S Zanar Azalan ◽  
T S Tengku Amran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Experimental Design ◽  
Data Collection ◽  
Ground Penetrating Radar ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Underground Utilities ◽  
Ground Penetrating ◽  
Non Destructive

Abstract This paper presents a review on Ground Penetrating Radar (GPR) detection and mapping of buried utilities which have been widely used as non-destructive investigation and efficiently in terms of usage. The reviews cover on experimental design in GPR data collection and survey, pre-processing, extracting hyperbolic feature using image processing and machine learning techniques. Some of the issues and challenges facing by the GPR interpretation particularly in extracting the hyperbolas pattern of underground utilities have also been highlighted.

scholarly journals Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Majid Amirfakhrian ◽  
Mahboub Parhizkar
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Machine Vision ◽  
Traffic Accidents ◽  
Fuzzy Theory ◽  
Machine Learning Techniques ◽  
Industrial Setting ◽  
Technological Advances ◽  
Learning Techniques ◽  
Wide Range

AbstractIn the next decade, machine vision technology will have an enormous impact on industrial works because of the latest technological advances in this field. These advances are so significant that the use of this technology is now essential. Machine vision is the process of using a wide range of technologies and methods in providing automated inspections in an industrial setting based on imaging, process control, and robot guidance. One of the applications of machine vision is to diagnose traffic accidents. Moreover, car vision is utilized for detecting the amount of damage to vehicles during traffic accidents. In this article, using image processing and machine learning techniques, a new method is presented to improve the accuracy of detecting damaged areas in traffic accidents. Evaluating the proposed method and comparing it with previous works showed that the proposed method is more accurate in identifying damaged areas and it has a shorter execution time.

Sign in / Sign up

Export Citation Format

Share Document