An Improved Deep Convolutional Neural Network-Based Autonomous Road Inspection Scheme Using Unmanned Aerial Vehicles

Recent advancements in the field of machine learning (ML) provide opportunity to conduct research on autonomous devices for a variety of applications. Intelligent decision-making is a critical task for self-driving systems. An attempt is made in this study to use a deep learning (DL) approach for th...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Syed-Ali Hassan, Tariq Rahim, Soo-Young Shin
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	autonomous navigation autonomous road inspection computer vision drone robots neural network Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/39613531cd9b4d27a2351df1484397b9
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:39613531cd9b4d27a2351df1484397b9
record_format	dspace
spelling	oai:doaj.org-article:39613531cd9b4d27a2351df1484397b92021-11-25T17:24:28ZAn Improved Deep Convolutional Neural Network-Based Autonomous Road Inspection Scheme Using Unmanned Aerial Vehicles10.3390/electronics102227642079-9292https://doaj.org/article/39613531cd9b4d27a2351df1484397b92021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/22/2764https://doaj.org/toc/2079-9292Recent advancements in the field of machine learning (ML) provide opportunity to conduct research on autonomous devices for a variety of applications. Intelligent decision-making is a critical task for self-driving systems. An attempt is made in this study to use a deep learning (DL) approach for the early detection of road cracks, potholes, and the yellow lane. The accuracy is not sufficient after training with the default model. To enhance accuracy, a convolutional neural network (CNN) model with 13 convolutional layers, a softmax layer as an output layer, and two fully connected layers (FCN) are constructed. In order to achieve the deeper propagation and to prevent saturation in the training phase, mish activation is employed in the first 12 layers with a rectified linear unit (ReLU) activation function. The upgraded CNN model performs better than the default CNN model in terms of accuracy. For the varied situation, a revised and enriched dataset for road cracks, potholes, and the yellow lane is created. The yellow lane is detected and tracked in order to move the unmanned aerial vehicle (UAV) autonomously by following yellow lane. After identifying a yellow lane, the UAV performs autonomous navigation while concurrently detecting road cracks and potholes using the robot operating system within the UAV. The performance model is benchmarked using performance measures, such as accuracy, sensitivity, <i>F</i>1-<i>score</i>, <i>F</i>2-<i>score</i>, and dice-coefficient, which demonstrate that the suggested technique produces better outcomes.Syed-Ali HassanTariq RahimSoo-Young ShinMDPI AGarticleautonomous navigationautonomous road inspectioncomputer visiondronerobotsneural networkElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2764, p 2764 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	autonomous navigation autonomous road inspection computer vision drone robots neural network Electronics TK7800-8360
spellingShingle	autonomous navigation autonomous road inspection computer vision drone robots neural network Electronics TK7800-8360 Syed-Ali Hassan Tariq Rahim Soo-Young Shin An Improved Deep Convolutional Neural Network-Based Autonomous Road Inspection Scheme Using Unmanned Aerial Vehicles
description	Recent advancements in the field of machine learning (ML) provide opportunity to conduct research on autonomous devices for a variety of applications. Intelligent decision-making is a critical task for self-driving systems. An attempt is made in this study to use a deep learning (DL) approach for the early detection of road cracks, potholes, and the yellow lane. The accuracy is not sufficient after training with the default model. To enhance accuracy, a convolutional neural network (CNN) model with 13 convolutional layers, a softmax layer as an output layer, and two fully connected layers (FCN) are constructed. In order to achieve the deeper propagation and to prevent saturation in the training phase, mish activation is employed in the first 12 layers with a rectified linear unit (ReLU) activation function. The upgraded CNN model performs better than the default CNN model in terms of accuracy. For the varied situation, a revised and enriched dataset for road cracks, potholes, and the yellow lane is created. The yellow lane is detected and tracked in order to move the unmanned aerial vehicle (UAV) autonomously by following yellow lane. After identifying a yellow lane, the UAV performs autonomous navigation while concurrently detecting road cracks and potholes using the robot operating system within the UAV. The performance model is benchmarked using performance measures, such as accuracy, sensitivity, <i>F</i>1-<i>score</i>, <i>F</i>2-<i>score</i>, and dice-coefficient, which demonstrate that the suggested technique produces better outcomes.
format	article
author	Syed-Ali Hassan Tariq Rahim Soo-Young Shin
author_facet	Syed-Ali Hassan Tariq Rahim Soo-Young Shin
author_sort	Syed-Ali Hassan
title	An Improved Deep Convolutional Neural Network-Based Autonomous Road Inspection Scheme Using Unmanned Aerial Vehicles
title_short	An Improved Deep Convolutional Neural Network-Based Autonomous Road Inspection Scheme Using Unmanned Aerial Vehicles
title_full	An Improved Deep Convolutional Neural Network-Based Autonomous Road Inspection Scheme Using Unmanned Aerial Vehicles
title_fullStr	An Improved Deep Convolutional Neural Network-Based Autonomous Road Inspection Scheme Using Unmanned Aerial Vehicles
title_full_unstemmed	An Improved Deep Convolutional Neural Network-Based Autonomous Road Inspection Scheme Using Unmanned Aerial Vehicles
title_sort	improved deep convolutional neural network-based autonomous road inspection scheme using unmanned aerial vehicles
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/39613531cd9b4d27a2351df1484397b9
work_keys_str_mv	AT syedalihassan animproveddeepconvolutionalneuralnetworkbasedautonomousroadinspectionschemeusingunmannedaerialvehicles AT tariqrahim animproveddeepconvolutionalneuralnetworkbasedautonomousroadinspectionschemeusingunmannedaerialvehicles AT sooyoungshin animproveddeepconvolutionalneuralnetworkbasedautonomousroadinspectionschemeusingunmannedaerialvehicles AT syedalihassan improveddeepconvolutionalneuralnetworkbasedautonomousroadinspectionschemeusingunmannedaerialvehicles AT tariqrahim improveddeepconvolutionalneuralnetworkbasedautonomousroadinspectionschemeusingunmannedaerialvehicles AT sooyoungshin improveddeepconvolutionalneuralnetworkbasedautonomousroadinspectionschemeusingunmannedaerialvehicles
_version_	1718412402540675072

An Improved Deep Convolutional Neural Network-Based Autonomous Road Inspection Scheme Using Unmanned Aerial Vehicles

Ejemplares similares