A Waste Management Technique to detect and separate Non-Biodegradable Waste using Machine Learning and YOLO algorithm

Aishwarya, Aishwarya; Wadhwa, Parth; Owais, Owais; Vashisht, Vasudha

doi:10.1109/confluence51648.2021.9377163

Cited by 15 publications

(4 citation statements)

References 10 publications

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“…It was observed that the accuracy of the employed algorithm is in the range of 94 to 96 percentages. Reference [26] proposed two approaches for the detection of the various types of waste containers.…”

Section: Introductionmentioning

confidence: 99%

GRUBin: Time-Series Forecasting-Based Efficient Garbage Monitoring and Management System for Smart Cities

Mishra

Vajire

Saxena

et al. 2022

Computational Intelligence and Neuroscience

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The waste management of an evolving smart city environment is one of the most important tasks as the living conditions and health of the population depend on proper waste management. Currently deployed systems are failing to monitor the garbage production as they use IoT-based pipelines to monitor the production in a locality, but often the device is used to get destroyed by the frequent use of dustbin. This leads to an increase in expenditure and affects the sustainability of the system. In this work, we propose an efficient and scalable garbage monitoring and collection methodology based on time-series forecasting techniques. The proposed system is also cost-effective because of the iterative deployment of rented IoT sensors, which are used to collect time series format data and then used to train the forecasting module to learn the temporal representation of the data that can produce accurate results for monitoring the fill-up time of the garbage collector. We also propose an efficient collection in-routing technique based on the ranking of bin stations on the basis of temporal and spatial data of the fill-up time and route location to minimize the collection time by making an efficient routing algorithm for garbage collection. This concept of garbage collection will be very useful for smart city planners.

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Section: Introductionmentioning

confidence: 99%

GRUBin: Time-Series Forecasting-Based Efficient Garbage Monitoring and Management System for Smart Cities

Mishra

Vajire

Saxena

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…Machine vision and YOLOv3 were used in [28] for the identification of marine plastics with a mean accuracy of 98%. YOLO methods have gained popularity for plastic and general waste identification, using vision sensors with accuracies ranging from 65% to 94.5% [29,30]. These vision-based approaches have potential for autonomous identification and sorting of plastics using robotic platforms.…”

Section: Related Workmentioning

confidence: 99%

Low-Cost Recognition of Plastic Waste Using Deep Learning and a Multi-Spectral Near-Infrared Sensor

Martinez-Hernandez,

West,

Assaf

2024

Sensors

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This work presents an approach for the recognition of plastics using a low-cost spectroscopy sensor module together with a set of machine learning methods. The sensor is a multi-spectral module capable of measuring 18 wavelengths from the visible to the near-infrared. Data processing and analysis are performed using a set of ten machine learning methods (Random Forest, Support Vector Machines, Multi-Layer Perceptron, Convolutional Neural Networks, Decision Trees, Logistic Regression, Naive Bayes, k-Nearest Neighbour, AdaBoost, Linear Discriminant Analysis). An experimental setup is designed for systematic data collection from six plastic types including PET, HDPE, PVC, LDPE, PP and PS household waste. The set of computational methods is implemented in a generalised pipeline for the validation of the proposed approach for the recognition of plastics. The results show that Convolutional Neural Networks and Multi-Layer Perceptron can recognise plastics with a mean accuracy of 72.50% and 70.25%, respectively, with the largest accuracy of 83.5% for PS plastic and the smallest accuracy of 66% for PET plastic. The results demonstrate that this low-cost near-infrared sensor with machine learning methods can recognise plastics effectively, making it an affordable and portable approach that contributes to the development of sustainable systems with potential for applications in other fields such as agriculture, e-waste recycling, healthcare and manufacturing.

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“…The proposed framework was also implemented in a real-world case study (Ton Duc Thang University) and the obtained results were promising in terms of time saved in waste collection and management. Aishwarya et al (2021) suggested the application of image processing based on YOLO and machine learning algorithms for detecting and separating non-biodegradable waste from the trash bins (Aishwarya et al, 2021). Data was divided into three major waste categories: plastic, metal, and glass with at least 450 images of each for training the model.…”

Section: Collection and Segregation Of Wastementioning

confidence: 99%

Recent advances in applications of artificial intelligence in solid waste management: A review

et al. 2022

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A Waste Management Technique to detect and separate Non-Biodegradable Waste using Machine Learning and YOLO algorithm

Cited by 15 publications

References 10 publications

GRUBin: Time-Series Forecasting-Based Efficient Garbage Monitoring and Management System for Smart Cities

GRUBin: Time-Series Forecasting-Based Efficient Garbage Monitoring and Management System for Smart Cities

Low-Cost Recognition of Plastic Waste Using Deep Learning and a Multi-Spectral Near-Infrared Sensor

Recent advances in applications of artificial intelligence in solid waste management: A review

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