A VGG-16 based Faster RCNN Model for PCB Error Inspection in Industrial AOI Applications

dc.citation.woscount	0	en_US
dc.contributor.author	Li, Yu Ting	en_US
dc.contributor.author	Guo, Jiun In	en_US
dc.contributor.department	電子工程學系及電子研究所	zh_TW
dc.contributor.department	Department of Electronics Engineering and Institute of Electronics	en_US
dc.date.accessioned	2019-04-02T06:04:48Z
dc.date.available	2019-04-02T06:04:48Z
dc.date.issued	2018-01-01	en_US
dc.description.abstract	To detect product error and modify the product error, most industry are using human eyes. However, it is not only costs time but also costs money. Our purpose is to develop a model to detect the PCB board errors and draw the bounding boxes. The model is going to be developed with a pre-trained model VGG16 and data collected from Adventech corp. The error types of training data have been speared into five error types (Bridge, Appearance, Empty, Solder_ball, Solder_balls), where the highest AP result of these classes is over 90%.	en_US
dc.identifier.issn	2381-5779	en_US
dc.identifier.journal	2018 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS-TAIWAN (ICCE-TW)	en_US
dc.identifier.uri	https://ir.lib.nycu.edu.tw/handle/11536/150937
dc.identifier.wosnumber	WOS:000454897600118	en_US
dc.language.iso	en_US	en_US
dc.title	A VGG-16 based Faster RCNN Model for PCB Error Inspection in Industrial AOI Applications	en_US
dc.type	Proceedings Paper	en_US

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