Abstract
The proper handling of waste is one of the biggest challenges of modern society. Municipal Solid Waste (MSW) requires categorization into a number of types, including bio, plastic, glass, metal, paper, etc. The most efficient techniques proposed by researchers so far include neural networks. In this paper, a detailed summarization was made of the existing deep learning techniques that have been proposed to classify waste. This paper proposes an architecture for the classification of litter into the categories specified in the benchmark approaches. The architecture used for classification was EfficientNet-B0. These are compound-scaling based models proposed by Google that are pretrained on ImageNet and have an accuracy of 74% to 84% in top-1 over ImageNet. This research proposes EfficientNet-B0 model tuning for images specific to particular demographic regions for efficient classification. This type of model tuning over transfer learning provides a customized model for classification, highly optimized for a particular region. It was shown that such a model had comparable accuracy to that of EfficientNet-B3, however, with a significantly smaller number of parameters required by the B3 model. Thus, the proposed technique achieved efficiency on the order of 4X in terms of FLOPS. Moreover, it resulted in improvised classifications as a result of fine-tuning over region-wise specific litter images.
Funder
Ministry of Science and Technology
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Reference26 articles.
1. Deep learning-based waste detection in natural and urban environments
2. Overview of Municipal Solid Waste Generation, Composition, and Management in India
3. Mass production of Scenedesmus incrassatulus in 8 and 40 liter disposable polyethylene bags with different culture media;Ceballos-Pinto;Rev. Latinoam. Microbiol.,1995
4. Integrated municipal solid waste scenario model using advanced pretreatment and waste to energy processes
5. Waste segregation using deep learning algorithm;Devi;Int. J. Innov. Technol. Explor. Eng.,2018
Cited by
45 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献