Waste plastic bottles classification with deep learning model
by Jixu Hou; Xiaofeng Xie; Wenwen Wang; Qian Cai; Zhengjie Deng; Houqun Yang; Hongnian Huang; Yizhen Wang
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 25, No. 3, 2023

Abstract: The misuse of plastic products has led to serious environment problem. To alleviate such phenomenon, we need to recover the plastic waste with a precise distinction. In this work, we applied a deep learning model, e.g., Faster-RCNN, to identify the class of plastic bottle. We have designed a waste plastic bottle recycling system, which can cooperate with the manipulator and conveyor to automatically sort the bottles in the garbage. During the experiment, we established a data set containing 8400 images. Different backbone networks are used to train on the data set. The experimental results show that the skeleton network using Resnet-50 as Faster-RCNN has higher detection performance than other networks. The system can also be applied to the identification and classification of other solid wastes.

Online publication date: Fri, 03-Nov-2023

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Wireless and Mobile Computing (IJWMC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com