Automation of Waste Sorting with Deep Learning

Sousa, João Borges de; Rebelo, Ana; Cardoso, Jaime S.

doi:10.1109/wvc.2019.8876924

Cited by 53 publications

(22 citation statements)

References 29 publications

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“…The network was trained for six objects classes (such as glass, cardboard, paper, metal, organic and plastic waste). Sousa et al [19] proposed a hierarchical DL model for waste classification and detection in food trays. The proposed 2 phase method maintains the benefits of current object detectors (as Faster RCNN) and permits the classification process to be assisted in high resolution bounding box.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification

Duhayyim¹,

Eisa²,

Al‐Wesabi³

et al. 2022

Computers, Materials &Amp; Continua

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The Smart City concept revolves around gathering real time data from citizen, personal vehicle, public transports, building, and other urban infrastructures like power grid and waste disposal system. The understandings obtained from the data can assist municipal authorities handle assets and services effectually. At the same time, the massive increase in environmental pollution and degradation leads to ecological imbalance is a hot research topic. Besides, the progressive development of smart cities over the globe requires the design of intelligent waste management systems to properly categorize the waste depending upon the nature of biodegradability. Few of the commonly available wastes are paper, paper boxes, food, glass, etc. In order to classify the waste objects, computer vision based solutions are cost effective to separate out the waste from the huge dump of garbage and trash. Due to the recent developments of deep learning (DL) and deep reinforcement learning (DRL), waste object classification becomes possible by the identification and detection of wastes. In this aspect, this paper designs an intelligence DRL based recycling waste object detection and classification (IDRL-RWODC) model for smart cities. The goal of the IDRL-RWODC technique is to detect and classify waste objects using the DL and DRL techniques. The IDRL-RWODC technique encompasses a twostage process namely Mask Regional Convolutional Neural Network (Mask RCNN) based object detection and DRL based object classification. In addition, DenseNet model is applied as a baseline model for the Mask RCNN model, and a deep Q-learning network (DQLN) is employed as a classifier.Moreover, a dragonfly algorithm (DFA) based hyperparameter optimizer is derived for improving the efficiency of the DenseNet model.

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Section: Literature Reviewmentioning

confidence: 99%

Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification

Duhayyim¹,

Eisa²,

Al‐Wesabi³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…As for those new optical sorting technologies for which interest is currently gaining momentum, VIS-sorting combined with AI-based image processing technologies represents one such technology [201][202][203]. There are already commercial robotic sorting solutions existing on the market [204,205].…”

Section: Exploring Future Perspectivesmentioning

confidence: 99%

Towards Higher Quality of Recycled Plastics: Limitations from the Material’s Perspective

et al. 2021

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The increasing consumption of plastics and plastic products results in correspondingly substantial volumes of waste, which poses considerable environmental burdens. With the ongoing environmental actions, the application of circular economy on this waste stream is becoming inevitable. In this paper, the topics of plastics recycling, circular economy on plastics, and challenges to plastic waste recycling are critically reviewed. In the first part of this paper, the development of research on plastic recycling was viewed from 1950 until 2020 using the scientific database Web of Science, and 682 related studies were found and used to assess the changing research priorities along that timeline. The following sections discuss the potentials and requirements to enhance the quality of the produced recycled plastic, in connection with the factors that currently limit it. In conclusion, the quality of recycled plastic is generally determined by the homogeneity of the recovered plastic feed. There are various strategies which could be implemented to overcome the hindrances identified in the paper and to improve the quality of the recycled plastic, such as working on enhanced product designs for minimised waste heterogeneity and controlling the materials’ degree of contamination by applying advanced sorting.

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“…But, the accuracy was not effective. Sousa et al (11) suggested a hierarchical Faster Region-based CNN (FR-CNN) for identifying and classifying the wastage in food trays. Also, a novel dataset called labeled wastage in the wild was collected and annotated for classification.…”

Section: Literature Surveymentioning

confidence: 99%

Enhanced segmentation network with deep learning for Biomedical waste classification

Mythili¹,

Anbarasi²

2021

IJST

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Objective: To maximize the accuracy of classifying the medical wastage, an Enhanced Segmentation Network (EnSegNet) with Deep Neural Network-Trash Classification (EnSegNet-DNN-TC) is proposed in this article. Methods: Initially, a core trainable segmentation network called SegNet framework is proposed which uses the Encoder-Decoder Network (EDN) and a pixel-wise classification layer for image segmentation. The decoder is used to upsample its low-resolution input feature maps via max-pooling. Also, SegNet uses fewer parameters for training. The uncertainty inherent to the EDN is modeled by the Bayesian functions to segment the input images. But, this SegNet can sample a limited amount of pixels in the images. Hence, an EnSegNet is proposed that uses Content-Sensitive Sampling (CSS) to sample more pixels in the data-sparse regions and fewer pixels in data-dense regions. Once the segmentation is completed, the DNN is applied for classifying the wastage using the segmented images. Findings: The experimental results show that the EnSegNet-DNN-TC framework achieves 88% accuracy compared to the DNN-TC for considering 100 images of different categories of biomedical wastes from the trash image dataset.

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Automation of Waste Sorting with Deep Learning

Cited by 53 publications

References 29 publications

Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification

Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification

Towards Higher Quality of Recycled Plastics: Limitations from the Material’s Perspective

Enhanced segmentation network with deep learning for Biomedical waste classification

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