Automatic detection and classification system of domestic waste via multimodel cascaded convolutional neural network

Jiajia Li, Jie Chen, Bin Sheng, Ping Li, Po Yang, David Dagan Feng, Jun Qi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)


Domestic waste classification was incorporated into legal provisions recently in China. However, relying on manpower to detect and classify domestic waste is highly inefficient. To that end, in this article, we propose a multimodel 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 multilabeled 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 is designed and applied to a Shanghai community, which helped to make waste recycling more efficient. Experimental results showed a state-of-the-art performance, with an average improvement of 10% in detection precision.

Original languageEnglish
Pages (from-to)163-173
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Issue number1
Publication statusPublished - Jan 2022


  • Detection precision
  • domestic waste detection and classification
  • multimodel cascaded convolutional neural network (MCCNN)
  • smart trash can (STC)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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