Multilabel image analysis on Polyethylene Terephthalate bottle images using PETNet Convolution Architecture

Khoirul Aziz; Inggis Kurnia Trisiawan; Kadek Dwi Suyasmini; Zendi Iklima; Mirna Yunita

doi:10.22441/sinergi.2023.2.003

Authors

Khoirul Aziz Department of Electrical Engineering, Faculty of Engineering, Universitas Mercu Buana, Indonesia
Inggis Kurnia Trisiawan Department of Electrical Engineering, Faculty of Engineering, Universitas Mercu Buana, Indonesia
Kadek Dwi Suyasmini Department of Electrical Engineering, Faculty of Engineering, Universitas Mercu Buana, Indonesia
Zendi Iklima Department of Electrical Engineering, Faculty of Engineering, Universitas Mercu Buana, Indonesia
Mirna Yunita Department of Computer Science, School of Computer Science and Technology, Beijing Institute of Technology, China

DOI:

https://doi.org/10.22441/sinergi.2023.2.003

Keywords:

Convolutional Neural Network (CNN), Polyethylene Terephthalate Network (PETNet), Quality Control, Transfer Learning Model, Visual Inspection,

Abstract

Packaging is one of the important aspects of the product. Good packaging can increase the competitiveness of a product. Therefore, to maintain the quality of the packaging of a product, it is necessary to have a visual inspection. Furthermore, an automatic visual inspection can reduce the occurrence of human errors in the manual inspection process. This research will use the convolution network to detect and classify PET (Polyethylene Terephthalate) bottles. The Convolutional Neural Network (CNN) method is one approach that can be used to detect and classify PET bottle packaging. This research was conducted by comparing seven network architecture models, namely VGG-16, Inception V3, MobileNet V2, Xception, Inception ResNet V2, Depthwise Separable Convolution (DSC), and PETNet, which is the architectural model proposed in this study. The results of this study indicate that the PETNet model gives the best results compared to other models, with a test score of 96.04%, by detecting and classifying 461 of 480 images with an average test time of 0.0016 seconds.