A comprehensive swarming intelligent method for optimizing deep learning-based object detection by unmanned ground vehicles.

Unmanned ground vehicles (UGVs) are an important research application of artificial intelligence. In particular, the deep learning-based object detection method is widely used in UGV-based environmental perception. Good experimental results are achieved by the deep learning-based object detection me...

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Autores principales:	Qian Xu, Gang Wang, Ying Li, Ling Shi, Yaxin Li
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/63cc1ad3edfd48cbb1e41de0be8b0dc8
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id	oai:doaj.org-article:63cc1ad3edfd48cbb1e41de0be8b0dc8
record_format	dspace
spelling	oai:doaj.org-article:63cc1ad3edfd48cbb1e41de0be8b0dc82021-12-02T20:05:34ZA comprehensive swarming intelligent method for optimizing deep learning-based object detection by unmanned ground vehicles.1932-620310.1371/journal.pone.0251339https://doaj.org/article/63cc1ad3edfd48cbb1e41de0be8b0dc82021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251339https://doaj.org/toc/1932-6203Unmanned ground vehicles (UGVs) are an important research application of artificial intelligence. In particular, the deep learning-based object detection method is widely used in UGV-based environmental perception. Good experimental results are achieved by the deep learning-based object detection method Faster region-based convolutional neural network (Faster R-CNN). However, the exploration space of the region proposal network (RPN) is restricted by its expression. In our paper, a boosted RPN (BRPN) with three improvements is developed to solve this problem. First, a novel enhanced pooling network is designed in this paper. Therefore, the BRPN can adapt to objects with different shapes. Second, the expression of BRPN loss function is improved to learn the negative samples. Furthermore, the grey wolf optimizer (GWO) is used to optimize the parameters of the improved BRPN loss function. Thereafter, the performance of the BRPN loss function is promoted. Third, a novel GA-SVM classifier is applied to strengthen the classification capacity. The PASCAL VOC 2007, VOC 2012 and KITTI datasets are used to test the BRPN. Consequently, excellent experimental results are obtained by our deep learning-based object detection method.Qian XuGang WangYing LiLing ShiYaxin LiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0251339 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Qian Xu Gang Wang Ying Li Ling Shi Yaxin Li A comprehensive swarming intelligent method for optimizing deep learning-based object detection by unmanned ground vehicles.
description	Unmanned ground vehicles (UGVs) are an important research application of artificial intelligence. In particular, the deep learning-based object detection method is widely used in UGV-based environmental perception. Good experimental results are achieved by the deep learning-based object detection method Faster region-based convolutional neural network (Faster R-CNN). However, the exploration space of the region proposal network (RPN) is restricted by its expression. In our paper, a boosted RPN (BRPN) with three improvements is developed to solve this problem. First, a novel enhanced pooling network is designed in this paper. Therefore, the BRPN can adapt to objects with different shapes. Second, the expression of BRPN loss function is improved to learn the negative samples. Furthermore, the grey wolf optimizer (GWO) is used to optimize the parameters of the improved BRPN loss function. Thereafter, the performance of the BRPN loss function is promoted. Third, a novel GA-SVM classifier is applied to strengthen the classification capacity. The PASCAL VOC 2007, VOC 2012 and KITTI datasets are used to test the BRPN. Consequently, excellent experimental results are obtained by our deep learning-based object detection method.
format	article
author	Qian Xu Gang Wang Ying Li Ling Shi Yaxin Li
author_facet	Qian Xu Gang Wang Ying Li Ling Shi Yaxin Li
author_sort	Qian Xu
title	A comprehensive swarming intelligent method for optimizing deep learning-based object detection by unmanned ground vehicles.
title_short	A comprehensive swarming intelligent method for optimizing deep learning-based object detection by unmanned ground vehicles.
title_full	A comprehensive swarming intelligent method for optimizing deep learning-based object detection by unmanned ground vehicles.
title_fullStr	A comprehensive swarming intelligent method for optimizing deep learning-based object detection by unmanned ground vehicles.
title_full_unstemmed	A comprehensive swarming intelligent method for optimizing deep learning-based object detection by unmanned ground vehicles.
title_sort	comprehensive swarming intelligent method for optimizing deep learning-based object detection by unmanned ground vehicles.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/63cc1ad3edfd48cbb1e41de0be8b0dc8
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A comprehensive swarming intelligent method for optimizing deep learning-based object detection by unmanned ground vehicles.

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