PotNet: Pothole detection for autonomous vehicle system using convolutional neural network

Abstract Advancement in vision‐based techniques has enabled the autonomous vehicle system (AVS) to understand the driving scene in depth. The capability of autonomous vehicle system to understand the scene, and detecting the specific object depends on the strong feature representation of such object...

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Autores principales:	Deepak Kumar Dewangan, Satya Prakash Sahu
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/3e8099d3ef224721a32420581c11a79c
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id	oai:doaj.org-article:3e8099d3ef224721a32420581c11a79c
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spelling	oai:doaj.org-article:3e8099d3ef224721a32420581c11a79c2021-11-16T10:15:44ZPotNet: Pothole detection for autonomous vehicle system using convolutional neural network1350-911X0013-519410.1049/ell2.12062https://doaj.org/article/3e8099d3ef224721a32420581c11a79c2021-01-01T00:00:00Zhttps://doi.org/10.1049/ell2.12062https://doaj.org/toc/0013-5194https://doaj.org/toc/1350-911XAbstract Advancement in vision‐based techniques has enabled the autonomous vehicle system (AVS) to understand the driving scene in depth. The capability of autonomous vehicle system to understand the scene, and detecting the specific object depends on the strong feature representation of such objects. However, pothole objects are difficult to identify due to their non‐uniform structure in challenging, and dynamic road environments. Existing approaches have shown limited performance for the precise detection of potholes. The study on the detection of potholes, and intelligent driving behaviour of autonomous vehicle system is little explored in existing articles. Hence, here, an improved prototype model, which is not only truly capable of detecting the potholes but also shows its intelligent driving behaviour when any pothole is detected, is proposed. The prototype is developed using a convolutional neural network with a vision camera to explore, and validates the potential, and autonomy of its driving behaviour in the prepared road environment. The experimental analysis of the proposed model on various performance measures have obtained accuracy, sensitivity, and F‐measure of 99.02%, 99.03%, and 98.33%, respectively, which are comparable with the available state‐of‐art techniques.Deepak Kumar DewanganSatya Prakash SahuWileyarticleElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENElectronics Letters, Vol 57, Iss 2, Pp 53-56 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Electrical engineering. Electronics. Nuclear engineering TK1-9971 Deepak Kumar Dewangan Satya Prakash Sahu PotNet: Pothole detection for autonomous vehicle system using convolutional neural network
description	Abstract Advancement in vision‐based techniques has enabled the autonomous vehicle system (AVS) to understand the driving scene in depth. The capability of autonomous vehicle system to understand the scene, and detecting the specific object depends on the strong feature representation of such objects. However, pothole objects are difficult to identify due to their non‐uniform structure in challenging, and dynamic road environments. Existing approaches have shown limited performance for the precise detection of potholes. The study on the detection of potholes, and intelligent driving behaviour of autonomous vehicle system is little explored in existing articles. Hence, here, an improved prototype model, which is not only truly capable of detecting the potholes but also shows its intelligent driving behaviour when any pothole is detected, is proposed. The prototype is developed using a convolutional neural network with a vision camera to explore, and validates the potential, and autonomy of its driving behaviour in the prepared road environment. The experimental analysis of the proposed model on various performance measures have obtained accuracy, sensitivity, and F‐measure of 99.02%, 99.03%, and 98.33%, respectively, which are comparable with the available state‐of‐art techniques.
format	article
author	Deepak Kumar Dewangan Satya Prakash Sahu
author_facet	Deepak Kumar Dewangan Satya Prakash Sahu
author_sort	Deepak Kumar Dewangan
title	PotNet: Pothole detection for autonomous vehicle system using convolutional neural network
title_short	PotNet: Pothole detection for autonomous vehicle system using convolutional neural network
title_full	PotNet: Pothole detection for autonomous vehicle system using convolutional neural network
title_fullStr	PotNet: Pothole detection for autonomous vehicle system using convolutional neural network
title_full_unstemmed	PotNet: Pothole detection for autonomous vehicle system using convolutional neural network
title_sort	potnet: pothole detection for autonomous vehicle system using convolutional neural network
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/3e8099d3ef224721a32420581c11a79c
work_keys_str_mv	AT deepakkumardewangan potnetpotholedetectionforautonomousvehiclesystemusingconvolutionalneuralnetwork AT satyaprakashsahu potnetpotholedetectionforautonomousvehiclesystemusingconvolutionalneuralnetwork
_version_	1718426539118297088

PotNet: Pothole detection for autonomous vehicle system using convolutional neural network

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