Research on Multiscene Vehicle Dataset Based on Improved FCOS Detection Algorithms

Whether in intelligent transportation or autonomous driving, vehicle detection is an important part. Vehicle detection still faces many problems, such as inaccurate vehicle detection positioning and low detection accuracy in complex scenes. FCOS as a representative of anchor-free detection algorithm...

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Autores principales:	Fei Yan, Hui Zhang, Tianyang Zhou, Zhiyong Fan, Jia Liu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/0d174a24bc4d4fa7b7edd8cdb7439927
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spelling	oai:doaj.org-article:0d174a24bc4d4fa7b7edd8cdb74399272021-11-29T00:55:30ZResearch on Multiscene Vehicle Dataset Based on Improved FCOS Detection Algorithms1099-052610.1155/2021/9167116https://doaj.org/article/0d174a24bc4d4fa7b7edd8cdb74399272021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9167116https://doaj.org/toc/1099-0526Whether in intelligent transportation or autonomous driving, vehicle detection is an important part. Vehicle detection still faces many problems, such as inaccurate vehicle detection positioning and low detection accuracy in complex scenes. FCOS as a representative of anchor-free detection algorithms was once a sensation, but now it seems to be slightly insufficient. Based on this situation, we propose an improved FCOS algorithm. The improvements are as follows: (1) we introduce a deformable convolution into the backbone to solve the problem that the receptive field cannot cover the overall goal; (2) we add a bottom-up information path after the FPN of the neck module to reduce the loss of information in the propagation process; (3) we introduce the balance module according to the balance principle, which reduces inconsistent detection of the bbox head caused by the mismatch of variance of different feature maps. To enhance the comparative experiment, we have extracted some of the most recent datasets from UA-DETRAC, COCO, and Pascal VOC. The experimental results show that our method has achieved good results on its dataset.Fei YanHui ZhangTianyang ZhouZhiyong FanJia LiuHindawi-WileyarticleElectronic computers. Computer scienceQA75.5-76.95ENComplexity, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronic computers. Computer science QA75.5-76.95
spellingShingle	Electronic computers. Computer science QA75.5-76.95 Fei Yan Hui Zhang Tianyang Zhou Zhiyong Fan Jia Liu Research on Multiscene Vehicle Dataset Based on Improved FCOS Detection Algorithms
description	Whether in intelligent transportation or autonomous driving, vehicle detection is an important part. Vehicle detection still faces many problems, such as inaccurate vehicle detection positioning and low detection accuracy in complex scenes. FCOS as a representative of anchor-free detection algorithms was once a sensation, but now it seems to be slightly insufficient. Based on this situation, we propose an improved FCOS algorithm. The improvements are as follows: (1) we introduce a deformable convolution into the backbone to solve the problem that the receptive field cannot cover the overall goal; (2) we add a bottom-up information path after the FPN of the neck module to reduce the loss of information in the propagation process; (3) we introduce the balance module according to the balance principle, which reduces inconsistent detection of the bbox head caused by the mismatch of variance of different feature maps. To enhance the comparative experiment, we have extracted some of the most recent datasets from UA-DETRAC, COCO, and Pascal VOC. The experimental results show that our method has achieved good results on its dataset.
format	article
author	Fei Yan Hui Zhang Tianyang Zhou Zhiyong Fan Jia Liu
author_facet	Fei Yan Hui Zhang Tianyang Zhou Zhiyong Fan Jia Liu
author_sort	Fei Yan
title	Research on Multiscene Vehicle Dataset Based on Improved FCOS Detection Algorithms
title_short	Research on Multiscene Vehicle Dataset Based on Improved FCOS Detection Algorithms
title_full	Research on Multiscene Vehicle Dataset Based on Improved FCOS Detection Algorithms
title_fullStr	Research on Multiscene Vehicle Dataset Based on Improved FCOS Detection Algorithms
title_full_unstemmed	Research on Multiscene Vehicle Dataset Based on Improved FCOS Detection Algorithms
title_sort	research on multiscene vehicle dataset based on improved fcos detection algorithms
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/0d174a24bc4d4fa7b7edd8cdb7439927
work_keys_str_mv	AT feiyan researchonmultiscenevehicledatasetbasedonimprovedfcosdetectionalgorithms AT huizhang researchonmultiscenevehicledatasetbasedonimprovedfcosdetectionalgorithms AT tianyangzhou researchonmultiscenevehicledatasetbasedonimprovedfcosdetectionalgorithms AT zhiyongfan researchonmultiscenevehicledatasetbasedonimprovedfcosdetectionalgorithms AT jialiu researchonmultiscenevehicledatasetbasedonimprovedfcosdetectionalgorithms
_version_	1718407752648228864

Research on Multiscene Vehicle Dataset Based on Improved FCOS Detection Algorithms

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