Data collecting, analysis, classification methods, and approaches of the road pavement defects detection

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F23%3A00011424" target="_blank" >RIV/46747885:24220/23:00011424 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/10253171" target="_blank" >https://ieeexplore.ieee.org/document/10253171</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICECCME57830.2023.10253171" target="_blank" >10.1109/ICECCME57830.2023.10253171</a>

Result language

angličtina

Original language name

Data collecting, analysis, classification methods, and approaches of the road pavement defects detection

Original language description

Imagining different spheres such as industry, medicine, education, and others undigitized nowadays is impossible. Wide digitalization leads to the significant growth of various data amounts. The data processing and analysis issue became a real challenge. This work is intended to show the author’s vision of the probable methods and solutions for the data array analysis. The article touches on accelerometer data analysis. It was decided to take road pavement defects identification and classification issue as a practical task to show the implementation of the theoretical assumptions. In this paper, the authors demonstrate the application of Recurrent Neural Networks such as Long Short-Term Memory Networks (LSTM), Convolutional Neural Networks LSTM (CNN-LSTM), and Convolutional LSTM (ConvLSTM) in the context of road pavement binary classification (defect or not a defect). Experiments have shown that sophisticated architectures (CNN-LSTM and ConvLSTM) compared to the basic LSTM have an 8-11% larger recall value; at the same time, comparing CNN-LSTM and ConvLSTM, according to the results of experiments, ConvLSTM has up to 13% increase in the precision metric, that is the best result among three Neural Networks described in the paper. Moreover, such aspects as the influence of the accelerometer position on the sensitivity of the sensor and acceleration data sets were also discussed.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10200 - Computer and information sciences

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering

ISBN

979-8-3503-2297-2

ISSN

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e-ISSN

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Number of pages

6

Pages from-to

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Publisher name

IEEE

Place of publication

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Event location

Tenerife, Canary Islands, Spain

Event date

Jan 1, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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