A Hybrid Model for Household Waste Sorting (HWS) Based on an Ensemble of Convolutional Neural Networks

Nengkai Wu, Gui Wang and Dongyao Jia ()
Additional contact information
Nengkai Wu: School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China
Gui Wang: China Construction Second Engineering Bureau Ltd., Beijing 101121, China
Dongyao Jia: School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China

Sustainability, 2024, vol. 16, issue 15, 1-18

Abstract: The exponential increase in waste generation is a significant global challenge with serious implications. Addressing this issue necessitates the enhancement of waste management processes. This study introduces a method that improves waste separation by integrating learning models at various levels. The method begins with the creation of image features as a new matrix using the Multi-Scale Local Binary Pattern (MLBP) technique. This technique optimally represents features and patterns across different scales. Following this, an ensemble model at the first level merges two Convolutional Neural Network (CNN) models, with each model performing the detection operation independently. A second-level CNN model is then employed to obtain the final output. This model uses the information from the first-level models and combines these features to perform a more accurate detection. The study’s novelty lies in the use of a second-level CNN model in the proposed ensemble system for fusing the results obtained from the first level, replacing conventional methods such as voting and averaging. Additionally, the study employs an MLBP feature selection approach for a more accurate description of the HW image features. It uses the Simulated Annealing (SA) algorithm for fine-tuning the hyperparameters of the CNN models, thereby optimizing the system’s performance. Based on the accuracy metric, the proposed method achieved an accuracy of 99.01% on the TrashNet dataset and 99.41% on the HGCD dataset. These results indicate a minimum improvement of 0.48% and 0.36%, respectively, compared to the other methods evaluated in this study.

Keywords: Household Waste Sorting (HWS); Multi-Scale Local Binary Pattern (MLBP); Convolutional Neural Network (CNN); deep learning; Simulated Annealing (SA) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/15/6500/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/15/6500/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:15:p:6500-:d:1445926

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().