Survey onidentification and classification of waste for  efficient disposal and recycling

M, Adhithya Prasanna; Kaushal, S.; Mahalakshmi, P

doi:10.14419/ijet.v7i2.8.10513

Cited by 20 publications

(7 citation statements)

References 3 publications

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“…Examination, identification and classification of waste for efficient disposal and recycling, using MATLAB and OPENCV software and SVM, CNN and KNN networks is reported in the literature. Some sources have categorized waste in six groups with recognition rate of 44.6% [15].…”

Section: Available Techniques For Manual and Automatic Waste Sortingmentioning

confidence: 99%

A Framework for Dry Waste Detection Based on a Deep Convolutional Neural Network

Ataee

Kazemitabar

Najafi

2020

IJEE

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Due to lack of proper regulations in many areas of the world, consumers are not mandated to waste sorting at the origin of the source. Moreover, human sorting often suffers from human errors and low accuracy. In the intelligent detection system, it is attempted to break down a variety of household wastes including plastic bottles, glass, metals, paper bags, compact plastics, paper and disposable containers. In this paper, a real waste image system is investigated using the deep convolutional neural network and a remarkable accuracy of 92.76% was achieved.

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Section: Available Techniques For Manual and Automatic Waste Sortingmentioning

confidence: 99%

A Framework for Dry Waste Detection Based on a Deep Convolutional Neural Network

Ataee

Kazemitabar

Najafi

2020

IJEE

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“…Most solutions are image-based, different techniques have been developed and used in waste classification as discussed in [8].…”

Section: Related Workmentioning

confidence: 99%

Automation of Waste Sorting with Deep Learning

Sousa

Rebelo

Cardoso

2019

2019 XV Workshop De Visão Computacional (WVC)

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Gostaria de expressar a minha sincera gratidão ao professor Jaime Cardoso, pela disponibilidade, e pelas suas ideias que elevaram este projeto. Também com a ajuda da professora Ana Rebelo, melhorei como estudante e engenheiro. Uma palavra ao INESC TEC e ao membros do CTM por me terem acolhido e ajudado sempre que necessário. Porque esta tese é também fruto dos últimos cinco anos, gostaria de agradecer ao professor Armando Sousa pelas experiências fantasticas e enriquecedoras na Universidade Júnior, Mostra UP e SPE. Aos amigos dos mortais encarpados, pelos trabalhos e principalmentes pelas pausas que fizemos juntos, aos que comigo escaparam civil para o mundo das eletrónicas, e aqueles com quem partilhei A aventura do outro lado do mundo, o meu sincero obrigado. Aos amigos de Sobrado, aos primos e família, que sempre tentaram que tivesse contacto com o mundo real, mesmo quando me fechava na FEUP. À Natacha, por toda a paciência nas ausências, pela ajuda técnica, por me ouvir mesmo quando nada percebia. Por toda a motivação e apoio, esta tese também é tua. Por fim, agradecer aos meus pais, por terem sido os melhores exemplos que poderia ter, pela educação, pelas viagens e oportunidades. O que sou hoje a eles lhes devo. João Soares Sousa v vi "Now this is not the end. It is not even the beginning of the end. But it is, perhaps, the end of the beginning."

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“…These facilities can help manage different types of waste more efficiently and minimize the adverse effects on the environment. Promote extended producer responsibility (EPR): EPR policies make manufacturers responsible for the end-of-life disposal of their products and packaging [ 1 , 2 , 3 ]. Implementing EPR could encourage manufacturers to design more eco-friendly products and packaging, leading to reduced waste generation.…”

Section: Introductionmentioning

confidence: 99%

A Design and Implementation Using an Innovative Deep-Learning Algorithm for Garbage Segregation

Gunaseelan,

Sundaram,

Mariyappan

2023

Sensors

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A startling shift in waste composition has been brought on by a dramatic change in lifestyle, the quick expansion of consumerism brought on by fierce competition among producers of consumer goods, and revolutionary advances in the packaging sector. The overflow or overspill of garbage from the bins causes poison to the soil, and the total obliteration of waste generated in the area or city is unknown. It is challenging to pinpoint with accuracy the specific sort of garbage waste; predictive image classification is lagging, and the existing approach takes longer to identify the specific garbage. To overcome this problem, image classification is carried out using a modified ResNeXt model. By adding a new block known as the “horizontal and vertical block,” the proposed ResNeXt architecture expands on the ResNet architecture. Each parallel branch of the block has its own unique collection of convolutional layers. Before moving on to the next layer, these branches are concatenated together. The block’s main goal is to expand the network’s capacity without considerably raising the number of parameters. ResNeXt is able to capture a wider variety of features in the input image by using parallel branches with various filter sizes, which improves performance on image classification. Some extra dense and dropout layers have been added to the standard ResNeXt model to improve performance. In order to increase the effectiveness of the network connections and decrease the total size of the model, the model is pruned to make it smaller. The overall architecture is trained and tested using garbage images. The convolution neural Network is connected with a modified ResNeXt that is trained using images of metal, trash, and biodegradable, and ResNet 50 is trained using images of non-biodegradable, glass, and hazardous images in a parallel way. An input image is fed to the architecture, and the image classification is achieved simultaneously to identify the exact garbage within a short time with an accuracy of 98%. The achieved results of the suggested method are demonstrated to be superior to those of the deep learning models already in use when compared to a variety of existing deep learning models. The proposed model is implemented into the hardware by designing a three-component smart bin system. It has three separate bins; it collects biodegradable, non-biodegradable, and hazardous waste separately. The smart bin has an ultrasonic sensor to detect the level of the bin, a poisonous gas sensor, a stepper motor to open the lid of the bin, a solar panel for battery storage, a Raspberry Pi camera, and a Raspberry Pi board. The levels of the bin are maintained in a centralized system for future analysis processes. The architecture used in the proposed smart bin properly disposes of the mixed garbage waste in an eco-friendly manner and recovers as much wealth as possible. It also reduces manpower, saves time, ensures proper collection of garbage from the bins, and helps attain a clean environment. The model boosts performance to predict waste generation and classify it with an increased 98.9% accuracy, which is more than the existing system.

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Survey onidentification and classification of waste for efficient disposal and recycling

Cited by 20 publications

References 3 publications

A Framework for Dry Waste Detection Based on a Deep Convolutional Neural Network

A Framework for Dry Waste Detection Based on a Deep Convolutional Neural Network

Automation of Waste Sorting with Deep Learning

A Design and Implementation Using an Innovative Deep-Learning Algorithm for Garbage Segregation

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