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A Study on the Outlet Blockage Determination Technology of Conveyor System using Deep Learning

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2020, 25(5), pp.11-18
  • DOI : 10.9708/jksci.2020.25.05.011
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : January 6, 2020
  • Accepted : April 24, 2020
  • Published : May 29, 2020

Jeong Eui-han 1 Suh Young Joo 1 Dong-Ju Kim 1

1포항공과대학교

Accredited

ABSTRACT

This study proposes a technique for the determination of outlet blockage using deep learning in a conveyor system. The proposed method aims to apply the best model to the actual process, where we train various CNN models for the determination of outlet blockage using images collected by CCTV in an industrial scene. We used the well-known CNN model such as VGGNet, ResNet, DenseNet and NASNet, and used 18,000 images collected by CCTV for model training and performance evaluation. As a experiment result with various models, VGGNet showed the best performance with 99.03% accuracy and 29.05ms processing time, and we confirmed that VGGNet is suitable for the determination of outlet blockage.

Citation status

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

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