TELKOMNIKA Telecommunication, Computing, Electronics and Control
Solid waste classification using pyramid scene parsing network segmentation and combined features

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Solid waste classification using pyramid scene parsing network segmentation and combined features

Feature extraction
PSPNet
Segmentation
SVM
Waste classification

Solid waste problem become a serious issue for the countries around the world
since the amount of generated solid waste increase annually. As an effort to
reduce and reuse of solid waste, a classification of solid waste image is needed
to support automatic waste sorting. In the image classification task, image
segmentation and feature extraction play important roles. This research applies
recent deep leaning-based segmentation, namely pyramid scene parsing
network (PSPNet). We also use various combination of image feature
extraction (color, texture, and shape) to search for the best combination of
features. As a comparison, we also perform experiment without using
segmentation to see the effect of PSPNet. Then, support vector machine
(SVM) is applied in the end as classification algorithm. Based on the result of
experiment, it can be concluded that generally applying segmentation provide
better source for feature extraction, especially in color and shape feature, hence
increase the accuracy of classifier. It is also observed that the most important
feature in this problem is color feature. However, the accuracy of classifier
increase if additional features are introduced. The highest accuracy of 76.49%
is achieved when PSPNet segmentation is applied and all combination of
features are used.

Khadijah, Sukmawati Nur Endah, Retno Kusumaningrum, Rismiyati, Priyo Sidik Sasongko,

Iffa Zainan Nisa

http://journal.uad.ac.id/index.php/TELKOMNIKA

Jun 5, 2021

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