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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

ISBN

979-8-3503-2297-2

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

—

Místo konání akce

Tenerife, Canary Islands, Spain

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—