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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%.

Citation status

* References for papers published after 2022 are currently being built.

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