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Paper Abstract and Keywords
Presentation 2022-10-30 16:00
A Trial of Recognition of Electronic Parts by Deep-Learning for Efficient Recycling
Yihong Tang, Tomonori Izumi (Ritsumeikan Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) In order to improve material-recycling of disposed electronic appliances, we aim to develop a system to analyze and categorize electronic boards and parts utilizing cameras over conveyor belt. We adopt deep learning technology to recognize part images. Since popular CNN models for general object recognition are over performing, we reduce and optimize the structure of the network to fit electronic parts. Our model classify a single part image within about 120[usec] with an accuracy of about 95[%].
Keyword (in Japanese) (See Japanese page) 
(in English) electronic wastes / recycling / deep learning / CNN / resource paradox / SDGs / /  
Reference Info. ITE Tech. Rep., vol. 46, pp. 19-22, Oct. 2022.
Paper #  
Date of Issue 2022-10-23 (AIT) 
ISSN Print edition: ISSN 1342-6893    Online edition: ISSN 2424-1970
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Conference Information
Committee IIEEJ AIT  
Conference Date 2022-10-30 - 2022-10-31 
Place (in Japanese) (See Japanese page) 
Place (in English) on line 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IIEEJ 
Conference Code 2022-10-IIEEJ-AIT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Trial of Recognition of Electronic Parts by Deep-Learning for Efficient Recycling 
Sub Title (in English)  
Keyword(1) electronic wastes  
Keyword(2) recycling  
Keyword(3) deep learning  
Keyword(4) CNN  
Keyword(5) resource paradox  
Keyword(6) SDGs  
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1st Author's Name Yihong Tang  
1st Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
2nd Author's Name Tomonori Izumi  
2nd Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
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Speaker Author-2 
Date Time 2022-10-30 16:00:00 
Presentation Time 20 minutes 
Registration for IIEEJ 
Paper # AIT2022-177 
Volume (vol) vol.46 
Number (no) no.34 
Page pp.19-22 
#Pages
Date of Issue 2022-10-23 (AIT) 


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