Integrated Circuit Board Object Detection and Image Augmentation Fusion Model Based on YOLO

Industry 4.0 has been a hot topic in recent years. The process of integrating Cyber-Physical Systems (CPS), Artificial Intelligence (AI), and Internet of Things (IoT) technology, will become the trend in future construction of smart factories. In the past, smart factories were developed around the c...

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Autores principales:	Szu-Yin Lin, Hao-Yu Li
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	smart manufacturing Internet of Things deep learning YOLO object recognition Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/fdf887f1d9f04562902f94df64f16be1
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id	oai:doaj.org-article:fdf887f1d9f04562902f94df64f16be1
record_format	dspace
spelling	oai:doaj.org-article:fdf887f1d9f04562902f94df64f16be12021-11-11T10:25:51ZIntegrated Circuit Board Object Detection and Image Augmentation Fusion Model Based on YOLO1662-521810.3389/fnbot.2021.762702https://doaj.org/article/fdf887f1d9f04562902f94df64f16be12021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnbot.2021.762702/fullhttps://doaj.org/toc/1662-5218Industry 4.0 has been a hot topic in recent years. The process of integrating Cyber-Physical Systems (CPS), Artificial Intelligence (AI), and Internet of Things (IoT) technology, will become the trend in future construction of smart factories. In the past, smart factories were developed around the concept of the Flexible Manufacturing System (FMS). Most parts of the quality management process still needed to be implemented by Automated Optical Inspection (AOI) methods which required human resources and time to perform second stage testing. Screening standards also resulted in the elimination of about 30% of the products. In this study, we sort and analyze several Region-based Convolutional Neural Network (R-CNN) and YOLO models that are currently more advanced and widely used, analyze the methods and development problems of the various models, and propose a suitable real-time image recognition model and architecture suitable for Integrated Circuit Board (ICB) in manufacturing process. The goal of the first stage of this study is to collect and use different types of ICBs as model training data sets, and establish a preliminary image recognition model that can classify and predict different types of ICBs based on different feature points. The second stage explores image augmentation fusion and optimization methods. The data augmentation method used in this study can reach an average accuracy of 96.53%. In the final stage, there is discussion of the applicability of the model to detect and recognize the ICB directionality in <1 s with a 98% accuracy rate to meet the real-time requirements of smart manufacturing. Accurate and instant object image recognition in the smart manufacturing process can save manpower required for testing, improve equipment effectiveness, and increase both the production capacity and the yield rate of the production line. The proposed model improves the overall manufacturing process.Szu-Yin LinHao-Yu LiFrontiers Media S.A.articlesmart manufacturingInternet of Thingsdeep learningYOLOobject recognitionNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neurorobotics, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	smart manufacturing Internet of Things deep learning YOLO object recognition Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	smart manufacturing Internet of Things deep learning YOLO object recognition Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Szu-Yin Lin Hao-Yu Li Integrated Circuit Board Object Detection and Image Augmentation Fusion Model Based on YOLO
description	Industry 4.0 has been a hot topic in recent years. The process of integrating Cyber-Physical Systems (CPS), Artificial Intelligence (AI), and Internet of Things (IoT) technology, will become the trend in future construction of smart factories. In the past, smart factories were developed around the concept of the Flexible Manufacturing System (FMS). Most parts of the quality management process still needed to be implemented by Automated Optical Inspection (AOI) methods which required human resources and time to perform second stage testing. Screening standards also resulted in the elimination of about 30% of the products. In this study, we sort and analyze several Region-based Convolutional Neural Network (R-CNN) and YOLO models that are currently more advanced and widely used, analyze the methods and development problems of the various models, and propose a suitable real-time image recognition model and architecture suitable for Integrated Circuit Board (ICB) in manufacturing process. The goal of the first stage of this study is to collect and use different types of ICBs as model training data sets, and establish a preliminary image recognition model that can classify and predict different types of ICBs based on different feature points. The second stage explores image augmentation fusion and optimization methods. The data augmentation method used in this study can reach an average accuracy of 96.53%. In the final stage, there is discussion of the applicability of the model to detect and recognize the ICB directionality in <1 s with a 98% accuracy rate to meet the real-time requirements of smart manufacturing. Accurate and instant object image recognition in the smart manufacturing process can save manpower required for testing, improve equipment effectiveness, and increase both the production capacity and the yield rate of the production line. The proposed model improves the overall manufacturing process.
format	article
author	Szu-Yin Lin Hao-Yu Li
author_facet	Szu-Yin Lin Hao-Yu Li
author_sort	Szu-Yin Lin
title	Integrated Circuit Board Object Detection and Image Augmentation Fusion Model Based on YOLO
title_short	Integrated Circuit Board Object Detection and Image Augmentation Fusion Model Based on YOLO
title_full	Integrated Circuit Board Object Detection and Image Augmentation Fusion Model Based on YOLO
title_fullStr	Integrated Circuit Board Object Detection and Image Augmentation Fusion Model Based on YOLO
title_full_unstemmed	Integrated Circuit Board Object Detection and Image Augmentation Fusion Model Based on YOLO
title_sort	integrated circuit board object detection and image augmentation fusion model based on yolo
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/fdf887f1d9f04562902f94df64f16be1
work_keys_str_mv	AT szuyinlin integratedcircuitboardobjectdetectionandimageaugmentationfusionmodelbasedonyolo AT haoyuli integratedcircuitboardobjectdetectionandimageaugmentationfusionmodelbasedonyolo
_version_	1718439185486970880

Integrated Circuit Board Object Detection and Image Augmentation Fusion Model Based on YOLO

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