Article 5420

Title of the article



Golovnin Oleg Konstantinovich, Candidate of engineering sciences, associate professor, sub-department of information systems and technologies, Samara National Research University (34 Moskovskoe shosse street, Samara, Russia),
Prokhorov Sergey Antonovich, Doctor of engineering sciences, professor, head of the sub-department of information systems and technologies, Samara National Research University (34 Moskovskoe shosse street, Samara, Russia),
Stolbova Anastasiya Aleksandrovna, Candidate of engineering sciences, associate professor, sub-department of information systems and technologies, Samara National Research University (34 Moskovskoe shosse street, Samara, Russia),

Index UDK





Background. The growth of mobile technologies led to the ability to survey the condition of road pavement on streets and highways using data received from sensors of mobile devices, which are not always reliable information sources. Thus, this work is focused on the improvement of road pavement defect detection in case of missing fragments in the initial data.
Materials and methods. The research proposes an approach to pavement defects detection based on the analysis of data collected by mobile devices. Due to the complexity of processing and transmission of video recordings from a mobile device's camera, the proposed approach uses data from an accelerometer and a GPS/GLONASS receiver. The unreliability of the accelerometers and possible failures in the transmission of measurement results over the cellular network lead to missing fragments in the initial data, that is why the proposed approach is based on the sequential implementation of Wavelet transform and intelligent neural network analysis, which together provides an increase in the probability of pavement defects detection.
Results. Based on the proposed approach, a system for survey the state of the road pavement was developed. Experimental testing of the system was carried out on two sections of the road network. Test sections are different in the quality of the pavement and speed modes.
Conclusions. The results of the study showed that the probability of road pavement detection was 0.95, thus, high rates were achieved relative to similar solutions. 

Key words

nonequidistance, accelerometer, sensor, neural network, mobile device.

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Дата создания: 17.02.2021 12:10
Дата обновления: 17.02.2021 13:21