Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud.

Low-end LiDAR sensor provides an alternative for depth measurement and object recognition for lightweight devices. However due to low computing capacity, complicated algorithms are incompatible to be performed on the device, with sparse information further limits the feature available for extraction...

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Autores principales:	Muhammad Rabani Mohd Romlay, Azhar Mohd Ibrahim, Siti Fauziah Toha, Philippe De Wilde, Ibrahim Venkat
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/24a9f0aeb87b4da0a1463a1cd31fbfa8
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spelling	oai:doaj.org-article:24a9f0aeb87b4da0a1463a1cd31fbfa82021-12-02T20:19:33ZNovel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud.1932-620310.1371/journal.pone.0256665https://doaj.org/article/24a9f0aeb87b4da0a1463a1cd31fbfa82021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0256665https://doaj.org/toc/1932-6203Low-end LiDAR sensor provides an alternative for depth measurement and object recognition for lightweight devices. However due to low computing capacity, complicated algorithms are incompatible to be performed on the device, with sparse information further limits the feature available for extraction. Therefore, a classification method which could receive sparse input, while providing ample leverage for the classification process to accurately differentiate objects within limited computing capability is required. To achieve reliable feature extraction from a sparse LiDAR point cloud, this paper proposes a novel Clustered Extraction and Centroid Based Clustered Extraction Method (CE-CBCE) method for feature extraction followed by a convolutional neural network (CNN) object classifier. The integration of the CE-CBCE and CNN methods enable us to utilize lightweight actuated LiDAR input and provides low computing means of classification while maintaining accurate detection. Based on genuine LiDAR data, the final result shows reliable accuracy of 97% through the method proposed.Muhammad Rabani Mohd RomlayAzhar Mohd IbrahimSiti Fauziah TohaPhilippe De WildeIbrahim VenkatPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0256665 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Muhammad Rabani Mohd Romlay Azhar Mohd Ibrahim Siti Fauziah Toha Philippe De Wilde Ibrahim Venkat Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud.
description	Low-end LiDAR sensor provides an alternative for depth measurement and object recognition for lightweight devices. However due to low computing capacity, complicated algorithms are incompatible to be performed on the device, with sparse information further limits the feature available for extraction. Therefore, a classification method which could receive sparse input, while providing ample leverage for the classification process to accurately differentiate objects within limited computing capability is required. To achieve reliable feature extraction from a sparse LiDAR point cloud, this paper proposes a novel Clustered Extraction and Centroid Based Clustered Extraction Method (CE-CBCE) method for feature extraction followed by a convolutional neural network (CNN) object classifier. The integration of the CE-CBCE and CNN methods enable us to utilize lightweight actuated LiDAR input and provides low computing means of classification while maintaining accurate detection. Based on genuine LiDAR data, the final result shows reliable accuracy of 97% through the method proposed.
format	article
author	Muhammad Rabani Mohd Romlay Azhar Mohd Ibrahim Siti Fauziah Toha Philippe De Wilde Ibrahim Venkat
author_facet	Muhammad Rabani Mohd Romlay Azhar Mohd Ibrahim Siti Fauziah Toha Philippe De Wilde Ibrahim Venkat
author_sort	Muhammad Rabani Mohd Romlay
title	Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud.
title_short	Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud.
title_full	Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud.
title_fullStr	Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud.
title_full_unstemmed	Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud.
title_sort	novel ce-cbce feature extraction method for object classification using a low-density lidar point cloud.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/24a9f0aeb87b4da0a1463a1cd31fbfa8
work_keys_str_mv	AT muhammadrabanimohdromlay novelcecbcefeatureextractionmethodforobjectclassificationusingalowdensitylidarpointcloud AT azharmohdibrahim novelcecbcefeatureextractionmethodforobjectclassificationusingalowdensitylidarpointcloud AT sitifauziahtoha novelcecbcefeatureextractionmethodforobjectclassificationusingalowdensitylidarpointcloud AT philippedewilde novelcecbcefeatureextractionmethodforobjectclassificationusingalowdensitylidarpointcloud AT ibrahimvenkat novelcecbcefeatureextractionmethodforobjectclassificationusingalowdensitylidarpointcloud
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Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud.

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