Application of deep learning object classifier to improve e-waste collection planning

A rapid rise in inhabitants across the globe has led to the inadmissible management of waste in various countries, giving rise to various health issues and environmental pollution. The waste-collecting trucks collect waste just once or twice in seven days. Due to improper waste collection practices, the waste in the dustbin is spread on the streets. Thus, to defeat this situation, an efficient solution for smart and effective waste management using machine learning (ML) and the Internet of Things (IoT) is proposed in this paper. In the proposed solution, the authors have used an Arduino UNO microcontroller, ultrasonic sensor, and moisture sensor. Using image processing, one can measure the waste index of a particular dumping ground. A hardware prototype is also developed for the proposed framework. Thus, the presented solution for the efficient management of waste accomplishes the aim of establishing clean and pollution-free cities.

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“…is strategy might be extended to other bulky garbage categories with comparable garbage collecting issues [28].…”

Section: Literature Reviewmentioning

confidence: 99%

[Retracted] Machine Learning and IoT‐Based Waste Management Model

Khan

Kumar

Srivastava³

et al. 2021

Computational Intelligence and Neuroscience

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“…Autonomous sorting of the litter using image processing helps to separate various types of waste from each other for recycling [ 9 , 20 , 31 , 41 , 44 ]. Also, deep neural networks are able to provide practical visual modelling tools for detection and classification of various types of waste [ 10 , 12 , 31 , 37 , 44 ]. Sakr et al [ 41 ] proposed that for reducing the waste contamination by other materials, the sorting and separation must be done as early as possible, and the need to automate this process is a significant facilitator for waste companies.…”

Section: Litter Management In Smart Eco-cyber-physical Systemsmentioning

confidence: 99%

Location-aware hazardous litter management for smart emergency governance in urban eco-cyber-physical systems

Peyvandi

Majidi

Peyvandi

et al. 2022

Multimed Tools Appl

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Smart city management is facing a new challenge from littered face masks during COVID-19 pandemic. Addressing the issues of detection and collection of this hazardous waste that is littered in public spaces and outside the controlled environments, usually associated with biomedical waste, is urgent for the safety of the communities around the world. Manual management of this waste is beyond the capabilities of governments worldwide as the geospatial scale of littering is very high and also because this contaminated litter is a health and safety issue for the waste collectors. In this paper, an autonomous biomedical waste management framework that uses edge surveillance and location intelligence for detection of the littered face masks and predictive modelling for emergency response to this problem is proposed. In this research a novel dataset of littered face masks in various conditions and environments is collected. Then, a new deep neural network architecture for rapid detection of discarded face masks on the video surveillance edge nodes is proposed. Furthermore, a location intelligence model for prediction of the areas with higher probability of hazardous litter in the smart city is presented. Experimental results show that the accuracy of the proposed model for detection of littered face masks in various environments is 96%, while the speed of processing is ten times faster than comparable models. The proposed framework can help authorities to plan for timely emergency response to scattering of hazardous material in residential environments.

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“…Nowakowski & Pamula (15) proposed a new method for classifying and identifying the e-wastage. In this method, CNN was applied for classifying the types of e-wastage whereas FR-CNN was used for identifying the type and size of the wastage equipment in the images.…”

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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Application of deep learning object classifier to improve e-waste collection planning

Cited by 162 publications

References 34 publications

[Retracted] Machine Learning and IoT‐Based Waste Management Model

[Retracted] Machine Learning and IoT‐Based Waste Management Model

Location-aware hazardous litter management for smart emergency governance in urban eco-cyber-physical systems

Enhanced segmentation network with deep learning for Biomedical waste classification

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