Improved YOLOv4-tiny network for real-time electronic component detection

Abstract In the electronics industry environment, rapid recognition of objects to be grasped from digital images is essential for visual guidance of intelligent robots. However, electronic components have a small size, are difficult to distinguish, and are in motion on a conveyor belt, making target...

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Autores principales:	Ce Guo, Xiao-ling Lv, Yan Zhang, Ming-lu Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/764eb43b9594409eb2bf5c2baf253dcf
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spelling	oai:doaj.org-article:764eb43b9594409eb2bf5c2baf253dcf2021-11-28T12:19:12ZImproved YOLOv4-tiny network for real-time electronic component detection10.1038/s41598-021-02225-y2045-2322https://doaj.org/article/764eb43b9594409eb2bf5c2baf253dcf2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02225-yhttps://doaj.org/toc/2045-2322Abstract In the electronics industry environment, rapid recognition of objects to be grasped from digital images is essential for visual guidance of intelligent robots. However, electronic components have a small size, are difficult to distinguish, and are in motion on a conveyor belt, making target detection more difficult. For this reason, the YOLOv4-tiny method is used to detect electronic components and is improved. Then, different network structures are built for the adaptive integration of middle- and high-level features to address the phenomenon in which the original algorithm integrates all feature information indiscriminately. The method is deployed on an electronic component dataset for validation. Experimental results show that the accuracy of the original algorithm is improved from 93.74 to 98.6%. Compared with other current mainstream algorithms, such as Faster RCNN, SSD, RefineDet, EfficientDet, and YOLOv4, the method can maintain high detection accuracy at the fastest speed. The method can provide a technical reference for the development of manufacturing robots in the electronics industry.Ce GuoXiao-ling LvYan ZhangMing-lu ZhangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ce Guo Xiao-ling Lv Yan Zhang Ming-lu Zhang Improved YOLOv4-tiny network for real-time electronic component detection
description	Abstract In the electronics industry environment, rapid recognition of objects to be grasped from digital images is essential for visual guidance of intelligent robots. However, electronic components have a small size, are difficult to distinguish, and are in motion on a conveyor belt, making target detection more difficult. For this reason, the YOLOv4-tiny method is used to detect electronic components and is improved. Then, different network structures are built for the adaptive integration of middle- and high-level features to address the phenomenon in which the original algorithm integrates all feature information indiscriminately. The method is deployed on an electronic component dataset for validation. Experimental results show that the accuracy of the original algorithm is improved from 93.74 to 98.6%. Compared with other current mainstream algorithms, such as Faster RCNN, SSD, RefineDet, EfficientDet, and YOLOv4, the method can maintain high detection accuracy at the fastest speed. The method can provide a technical reference for the development of manufacturing robots in the electronics industry.
format	article
author	Ce Guo Xiao-ling Lv Yan Zhang Ming-lu Zhang
author_facet	Ce Guo Xiao-ling Lv Yan Zhang Ming-lu Zhang
author_sort	Ce Guo
title	Improved YOLOv4-tiny network for real-time electronic component detection
title_short	Improved YOLOv4-tiny network for real-time electronic component detection
title_full	Improved YOLOv4-tiny network for real-time electronic component detection
title_fullStr	Improved YOLOv4-tiny network for real-time electronic component detection
title_full_unstemmed	Improved YOLOv4-tiny network for real-time electronic component detection
title_sort	improved yolov4-tiny network for real-time electronic component detection
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/764eb43b9594409eb2bf5c2baf253dcf
work_keys_str_mv	AT ceguo improvedyolov4tinynetworkforrealtimeelectroniccomponentdetection AT xiaolinglv improvedyolov4tinynetworkforrealtimeelectroniccomponentdetection AT yanzhang improvedyolov4tinynetworkforrealtimeelectroniccomponentdetection AT mingluzhang improvedyolov4tinynetworkforrealtimeelectroniccomponentdetection
_version_	1718408083760218112

Improved YOLOv4-tiny network for real-time electronic component detection

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