Multi-material classification of dry recyclables from municipal solid waste based on thermal imaging

Gundupalli, Sathish Paulraj; Hait, Subrata; Thakur, Ajay Singh

doi:10.1016/j.wasman.2017.09.019

Cited by 49 publications

(17 citation statements)

References 39 publications

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“…Some unsuccessful recognition result images of paper and comparison images are shown in Figure 8. e feature information of Figure 8 is shown in Tables 6 and 7, where these numbers (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) are characteristic information number (see Section 2.1 for details).…”

Section: Some Unsuccessful Recognition Results Images Andmentioning

confidence: 99%

“…Laser-induced breakdown spectrometry together with discriminant function analysis was used for the identification and classification of five groups of polymers in the electronics industry [1]. A thermal imagingbased system for classifying useful recyclables from simulated municipal solid waste (MSW) sample was reported, and the system could be used in recycling plants for processing MSW in developing countries [2]. An intelligent waste material classification system was proposed, which was developed by using the 50-layer residual net pretrained convolutional neural network (CNN) model [3].…”

Section: Introductionmentioning

confidence: 99%

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Study on Waste Type Identification Method Based on Bird Flock Neural Network

Chen

2020

Mathematical Problems in Engineering

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According to the waste type identification requirement in waste classification, a waste type identification method based on a bird flock neural network (BFNN) was proposed. The problem of obtaining the feature dataset of waste images was considered, and color histogram and texture feature extraction techniques were used. The local optimum problem of a typical backpropagation neural network (BPNN) was considered, and a bird flock optimization (BFO) algorithm was proposed. The accuracy problem of the typical BPNN was considered, and a new online weight adjustment method of neurons was proposed. The number of hidden layer neurons (nodes) of the typical BPNN was considered, and an online adjustment method was proposed. The experimental results show that the recyclables (paper, plastic, glass, and cloth) and nonrecyclables can effectively be identified by the waste type identification method based on the BFNN, and the recognition accuracy is 81% which meets actual needs.

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Section: Some Unsuccessful Recognition Results Images Andmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Study on Waste Type Identification Method Based on Bird Flock Neural Network

Chen

2020

Mathematical Problems in Engineering

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“…Fourier Transform Raman Spectroscopy has been shown to accommodate shape, color, surface state in discriminating polymers (Florestan, 1994). Furthermore thermal imaging (a type of IR) could be used as an inexpensive dirty environment, robust method to separate single wastes streams into broad categories of dry recyclables (Gundupalli, 2017b). Finally, NIR hyper spectral imaging (HSI) technology digitally captures and analyzes plastic spectra by physical and chemical characteristics (Zheng, 2018).…”

Section: Emerging Technologiesmentioning

confidence: 99%

Separation and processing of plastic films

Reed

Lacey

Thompson

2018

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2016).There is very little incentive to companies to develop an improved separation process to remove and collect FPP when the cost to benefit ratio may be unfavorable, even if it is environmentally green. This report includes a current state of the scientific and trade literature and entertains topics about the challenges of FPP separation including variables such as chemical composition, amounts, types, waste streams, contamination, fate, and current and emerging detection technologies. Additionally, equipment manufacturers and MRFs have been contacted to identify and evaluate their methods or approaches for plastic film separation and recovery. Some of their solutions that have been implemented are discussed and potential strategies are suggested to reduce the growing FPP waste problems of the future.

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“…Currently, employed methods rely on manual separation, classification, and transportation, which require a lot of manual labor and money, hence making it a cumbersome and expensive process [1]. Several techniques of automatic waste detection and sorting have been proposed, such as automated sorting [2][3][4][5]. Hence, a massive amount of potential lies in the domain of waste management techniques and improving them.…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach based hardware solution to categorise garbage in environment

Gupta

Joshi

Mukhopadhyay

et al. 2021

Complex Intell. Syst.

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Garbage detection and disposal have become one of the major hassles in urban planning. Due to population influx in urban areas, the rate of garbage generation has increased exponentially along with garbage diversity. In this paper, we propose a hardware solution for garbage segregation at the base level based on deep learning architecture. The proposed deep-learning-based hardware solution SmartBin can segregate the garbage into biodegradable and non-biodegradable using Image classification through a Convolutional Neural Network System Architecture using a Real-time embedded system. Garbage detection via image classification aims for quick and efficient categorization of garbage present in the bin. However, this is an arduous task as garbage can be of any dimension, object, color, texture, unlike object detection of a particular entity where images of objects of that entity do share some similar characteristics and traits. The objective of this work is to compare the performance of various pre-trained Convolution Neural Network namely AlexNet, ResNet, VGG-16, and InceptionNet for garbage classification and test their working along with hardware components (PiCam, raspberry pi, infrared sensors, etc.) used for garbage detection in the bin. The InceptionNet Neural Network showed the best performance measure for the proposed model with an accuracy of 98.15% and a loss of 0.10 for the training set while it was 96.23% and 0.13 for the validation set.

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Multi-material classification of dry recyclables from municipal solid waste based on thermal imaging

Cited by 49 publications

References 39 publications

Study on Waste Type Identification Method Based on Bird Flock Neural Network

Study on Waste Type Identification Method Based on Bird Flock Neural Network

Separation and processing of plastic films

A deep learning approach based hardware solution to categorise garbage in environment

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