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MobileYOLO: Real-Time Object Detection Algorithm in Autonomous Driving Scenarios

Object detection is one of the key tasks in an automatic driving system. Aiming to solve the problem of object detection, which cannot meet the detection speed and detection accuracy at the same time, a real-time object detection algorithm (MobileYOLO) is proposed based on YOLOv4. Firstly, the featu...

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Detalles Bibliográficos
Autores principales: Zhou, Yan, Wen, Sijie, Wang, Dongli, Meng, Jiangnan, Mu, Jinzhen, Irampaye, Richard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9100546/
https://www.ncbi.nlm.nih.gov/pubmed/35591039
http://dx.doi.org/10.3390/s22093349
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author Zhou, Yan
Wen, Sijie
Wang, Dongli
Meng, Jiangnan
Mu, Jinzhen
Irampaye, Richard
author_facet Zhou, Yan
Wen, Sijie
Wang, Dongli
Meng, Jiangnan
Mu, Jinzhen
Irampaye, Richard
author_sort Zhou, Yan
collection PubMed
description Object detection is one of the key tasks in an automatic driving system. Aiming to solve the problem of object detection, which cannot meet the detection speed and detection accuracy at the same time, a real-time object detection algorithm (MobileYOLO) is proposed based on YOLOv4. Firstly, the feature extraction network is replaced by introducing the MobileNetv2 network to reduce the number of model parameters; then, part of the standard convolution is replaced by depthwise separable convolution in PAnet and the head network to further reduce the number of model parameters. Finally, by introducing an improved lightweight channel attention modul—Efficient Channel Attention (ECA)—to improve the feature expression ability during feature fusion. The Single-Stage Headless (SSH) context module is introduced to the small object detection branch to increase the receptive field. The experimental results show that the improved algorithm has an accuracy rate of 90.7% on the KITTI data set. Compared with YOLOv4, the parameters of the proposed MobileYOLO model are reduced by 52.11 M, the model size is reduced to one-fifth, and the detection speed is increased by 70%.
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spelling pubmed-91005462022-05-14 MobileYOLO: Real-Time Object Detection Algorithm in Autonomous Driving Scenarios Zhou, Yan Wen, Sijie Wang, Dongli Meng, Jiangnan Mu, Jinzhen Irampaye, Richard Sensors (Basel) Article Object detection is one of the key tasks in an automatic driving system. Aiming to solve the problem of object detection, which cannot meet the detection speed and detection accuracy at the same time, a real-time object detection algorithm (MobileYOLO) is proposed based on YOLOv4. Firstly, the feature extraction network is replaced by introducing the MobileNetv2 network to reduce the number of model parameters; then, part of the standard convolution is replaced by depthwise separable convolution in PAnet and the head network to further reduce the number of model parameters. Finally, by introducing an improved lightweight channel attention modul—Efficient Channel Attention (ECA)—to improve the feature expression ability during feature fusion. The Single-Stage Headless (SSH) context module is introduced to the small object detection branch to increase the receptive field. The experimental results show that the improved algorithm has an accuracy rate of 90.7% on the KITTI data set. Compared with YOLOv4, the parameters of the proposed MobileYOLO model are reduced by 52.11 M, the model size is reduced to one-fifth, and the detection speed is increased by 70%. MDPI 2022-04-27 /pmc/articles/PMC9100546/ /pubmed/35591039 http://dx.doi.org/10.3390/s22093349 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhou, Yan
Wen, Sijie
Wang, Dongli
Meng, Jiangnan
Mu, Jinzhen
Irampaye, Richard
MobileYOLO: Real-Time Object Detection Algorithm in Autonomous Driving Scenarios
title MobileYOLO: Real-Time Object Detection Algorithm in Autonomous Driving Scenarios
title_full MobileYOLO: Real-Time Object Detection Algorithm in Autonomous Driving Scenarios
title_fullStr MobileYOLO: Real-Time Object Detection Algorithm in Autonomous Driving Scenarios
title_full_unstemmed MobileYOLO: Real-Time Object Detection Algorithm in Autonomous Driving Scenarios
title_short MobileYOLO: Real-Time Object Detection Algorithm in Autonomous Driving Scenarios
title_sort mobileyolo: real-time object detection algorithm in autonomous driving scenarios
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9100546/
https://www.ncbi.nlm.nih.gov/pubmed/35591039
http://dx.doi.org/10.3390/s22093349
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