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http://dx.doi.org/10.9708/jksci.2020.25.05.011

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

Jeong, Eui-Han (Postech Institute of Artificial Intelligence, POSTECH)
Suh, Young-Joo (Dept. of Computer Science and Engineering, POSTECH)
Kim, Dong-Ju (Postech Institute of Artificial Intelligence, POSTECH)
Publication Information
Journal of the Korea Society of Computer and Information / v.25, no.5, 2020 , pp. 11-18 More about this Journal
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.
Keywords
Conveyor Systems; Blockage Determination; Deep Learning; Convolutional Neural Network(CNN); Visual Geometry Group Network(VGGNet); Residual Network(ResNet); Dense Network(DenseNet); Neural Architecture Search Network(NASNet);
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