A combination model based on transfer learning for waste classification. (14th April 2020)

• Record Type: Journal Article
• Authors: Huang, Guang‐Li; He, Jing; Xu, Zenglin; Huang, Guangyan
• Is Part Of: Concurrency and computation. Volume 32:Number 19(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-04-14
• Subjects: combined model -- solid waste -- transfer learning -- waste classification; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5751
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The increasing amount of solid waste is becoming a significant problem that needs to be addressed urgently. The reliable and accurate classification method is a crucial step in waste disposal because different types of wastes have different disposal ways. The existing waste classification models driven by deep learning are not easy to achieve accurate results and still need to be improved due to the various architecture networks adopted. Their performance on different datasets is varied, and there is also a lack of specific large‐scale datasets for training. We propose a new combination classification model based on three pretrained CNN models (VGG19, DenseNet169, and NASNetLarge) for processing the ImageNet database and achieve high classification accuracy. In our proposed model, the transfer learning model based on each pretrained model is constructed as a candidate classifier, and the optimal output of three candidate classifiers is selected as the final classification result. The experiments based on two waste image datasets demonstrate that the proposed model achieves 96.5% and 94% classification accuracy and outperforms several counterpart methods.