Li, J., Chen, J., Sheng, B. et al. (4 more authors) (2022) Automatic detection and classification system of domestic waste via multi-model cascaded convolutional neural network. IEEE Transactions on Industrial Informatics, 18 (1). pp. 163-173. ISSN 1551-3203
Abstract
Domestic waste classification was incorporated into legal provisions recently in China. The workforce for domestic waste detection and classification is inefficient. We proposed a Multi-model Cascaded Convolutional Neural Network (MCCNN) for domestic waste image detection and classification. MCCNN combined three subnetworks (DSSD, YOLOv4, and Faster-RCNN) to obtain the detections. Moreover, to suppress the false-positive predicts, we utilized a classification model cascaded with the detection part to judge whether the detection results are correct. To train and evaluate MCCNN, we designed a large-scale waste image dataset (LSWID), containing 30,000 domestic waste multi-labeled images with 52 categories. To the best of our knowledge, the LSWID is the largest dataset on domestic waste images. Furthermore, a smart trash can (STC) is designed and applied to a Shanghai community, which helped save time and make waste recycling more efficient. Experimental results showed a state-of-the-art performance, with an average improvement of 10% in detection precision.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Domestic waste detection and classification; multi-model cascaded convolutional neural network; detection precision; smart trash can |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Jun 2021 07:52 |
Last Modified: | 01 Jun 2022 00:38 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/TII.2021.3085669 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:174626 |