Remote drain inspection framework using the convolutional neural network and re-configurable robot Raptor

Abstract Drain blockage is a crucial problem in the urban environment. It heavily affects the ecosystem and human health. Hence, routine drain inspection is essential for urban environment. Manual drain inspection is a tedious task and prone to accidents and water-borne diseases. This work presents...

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Autores principales:	Lee Ming Jun Melvin, Rajesh Elara Mohan, Archana Semwal, Povendhan Palanisamy, Karthikeyan Elangovan, Braulio Félix Gómez, Balakrishnan Ramalingam, Dylan Ng Terntzer
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7958d0f6fd4b4d489bd94d3cf8b6a4a7
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spelling	oai:doaj.org-article:7958d0f6fd4b4d489bd94d3cf8b6a4a72021-11-21T12:20:27ZRemote drain inspection framework using the convolutional neural network and re-configurable robot Raptor10.1038/s41598-021-01170-02045-2322https://doaj.org/article/7958d0f6fd4b4d489bd94d3cf8b6a4a72021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01170-0https://doaj.org/toc/2045-2322Abstract Drain blockage is a crucial problem in the urban environment. It heavily affects the ecosystem and human health. Hence, routine drain inspection is essential for urban environment. Manual drain inspection is a tedious task and prone to accidents and water-borne diseases. This work presents a drain inspection framework using convolutional neural network (CNN) based object detection algorithm and in house developed reconfigurable teleoperated robot called ‘Raptor’. The CNN based object detection model was trained using a transfer learning scheme with our custom drain-blocking objects data-set. The efficiency of the trained CNN algorithm and drain inspection robot Raptor was evaluated through various real-time drain inspection field trial. The experimental results indicate that our trained object detection algorithm has detect and classified the drain blocking objects with 91.42% accuracy for both offline and online test images and is able to process 18 frames per second (FPS). Further, the maneuverability of the robot was evaluated from various open and closed drain environment. The field trial results ensure that the robot maneuverability was stable, and its mapping and localization is also accurate in a complex drain environment.Lee Ming Jun MelvinRajesh Elara MohanArchana SemwalPovendhan PalanisamyKarthikeyan ElangovanBraulio Félix GómezBalakrishnan RamalingamDylan Ng TerntzerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lee Ming Jun Melvin Rajesh Elara Mohan Archana Semwal Povendhan Palanisamy Karthikeyan Elangovan Braulio Félix Gómez Balakrishnan Ramalingam Dylan Ng Terntzer Remote drain inspection framework using the convolutional neural network and re-configurable robot Raptor
description	Abstract Drain blockage is a crucial problem in the urban environment. It heavily affects the ecosystem and human health. Hence, routine drain inspection is essential for urban environment. Manual drain inspection is a tedious task and prone to accidents and water-borne diseases. This work presents a drain inspection framework using convolutional neural network (CNN) based object detection algorithm and in house developed reconfigurable teleoperated robot called ‘Raptor’. The CNN based object detection model was trained using a transfer learning scheme with our custom drain-blocking objects data-set. The efficiency of the trained CNN algorithm and drain inspection robot Raptor was evaluated through various real-time drain inspection field trial. The experimental results indicate that our trained object detection algorithm has detect and classified the drain blocking objects with 91.42% accuracy for both offline and online test images and is able to process 18 frames per second (FPS). Further, the maneuverability of the robot was evaluated from various open and closed drain environment. The field trial results ensure that the robot maneuverability was stable, and its mapping and localization is also accurate in a complex drain environment.
format	article
author	Lee Ming Jun Melvin Rajesh Elara Mohan Archana Semwal Povendhan Palanisamy Karthikeyan Elangovan Braulio Félix Gómez Balakrishnan Ramalingam Dylan Ng Terntzer
author_facet	Lee Ming Jun Melvin Rajesh Elara Mohan Archana Semwal Povendhan Palanisamy Karthikeyan Elangovan Braulio Félix Gómez Balakrishnan Ramalingam Dylan Ng Terntzer
author_sort	Lee Ming Jun Melvin
title	Remote drain inspection framework using the convolutional neural network and re-configurable robot Raptor
title_short	Remote drain inspection framework using the convolutional neural network and re-configurable robot Raptor
title_full	Remote drain inspection framework using the convolutional neural network and re-configurable robot Raptor
title_fullStr	Remote drain inspection framework using the convolutional neural network and re-configurable robot Raptor
title_full_unstemmed	Remote drain inspection framework using the convolutional neural network and re-configurable robot Raptor
title_sort	remote drain inspection framework using the convolutional neural network and re-configurable robot raptor
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7958d0f6fd4b4d489bd94d3cf8b6a4a7
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Remote drain inspection framework using the convolutional neural network and re-configurable robot Raptor

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