Deep Learning based Scrapbox Accumulated Status Measuring (딥러닝 기반의 스크랩박스 적치 상태 측정 기술 개발)

Ye-In Seo (서예인); Jeong Eui-han (정의한); Dong-Ju Kim (김동주)

doi:10.9708/jksci.2020.25.03.027

Deep Learning based Scrapbox Accumulated Status Measuring

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2020, 25(3), pp.27-32
DOI : 10.9708/jksci.2020.25.03.027
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : January 6, 2020
Accepted : February 25, 2020
Published : March 31, 2020

Ye-In Seo ¹, Jeong Eui-han ¹, Dong-Ju Kim ¹

¹포항공과대학교

Accredited

ABSTRACT

In this paper, we propose an algorithm to measure the accumulated status of scrap boxes where metal scraps are accumulated. The accumulated status measuring is defined as a multi-class classification problem, and the method with deep learning classify the accumulated status using only the scrap box image. The learning was conducted by the Transfer Learning method, and the deep learning model was NASNet-A. In order to improve the accuracy of the model, we combined the Random Forest classifier with the trained NASNet-A and improved the model through post-processing. Testing with 4,195 data collected in the field showed 55% accuracy when only NASNet-A was applied, and the proposed method, NASNet with Random Forest, improved the accuracy by 88%.

KEYWORDS

Deep Learning, Accumulated status Measuring, CNN, Transfer Learning, Machine Learning

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* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

KJCKorea
Journal Central

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Deep Learning based Scrapbox Accumulated Status Measuring

ABSTRACT

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Deep Learning based Scrapbox Accumulated Status Measuring

ABSTRACT

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* References for papers published after 2023 are currently being built.