Abstract
This study proposes a multilayer hybrid deep-learning system (MHS) to automatically sort waste disposed of by individuals in the urban public area. This system deploys a high-resolution camera to capture waste image and sensors to detect other useful feature information. The MHS uses a CNN-based algorithm to extract image features and a multilayer perceptrons (MLP) method to consolidate image features and other feature information to classify wastes as recyclable or the others. The MHS is trained and validated against the manually labelled items, achieving overall classification accuracy higher than 90% under two different testing scenarios, which significantly outperforms a reference CNN-based method relying on image-only inputs. Copyright © 2018 Yinghao Chu et al.
| Original language | English |
|---|---|
| Article number | 5060857 |
| Journal | Computational Intelligence and Neuroscience |
| Volume | 2018 |
| DOIs | |
| Publication status | Published - 2018 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Funding
This work was partly financially supported by the National Natural Science Foundation of China (Grant no. 11702073), Shenzhen Key Lab Fund of Mechanisms and Control in Aerospace (Grant no. ZDSYS201703031002066), and the Basic Research Plan of Shenzhen (Grant nos. JCYJ20170413112645981 and JCYJ20170811160440239).
Publisher's Copyright Statement
- This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/