Next Generation Smart Garbage Level Indication and Monitoring System using IoT

Ravanan, Velnath; Subasri, R; Kumar, Mahesh; Dhivya, K.T.; Kumar, Papendra; Roobini, K

doi:10.1109/stcr51658.2021.9588961

Cited by 12 publications

(1 citation statement)

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“…Ravanan et al [19], proposed an IoT-based smart waste monitoring system. The system monitors waste bins with ultrasonic sensors and provides information about the amount of waste through a web page.…”

Section: Introductionmentioning

confidence: 99%

Yolov3 Tabanlı Atık Tespit Sistemleri

KARACA,

UZUN,

KAÇAR

2023

Journal of Smart Systems Research

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Today, the increase in the number of people, advances in industry and technology cause an increase in the number of wastes generated with the acceleration of production. It is important for the future of our country and the world that these wastes are more easily identified and recycled. In the process of recycling wastes, the classification of wastes as well as their collection requires costly energy and manpower. Wastes are basically separated into paper, plastic, glass and metal. Various studies have been carried out to complete these processes in a shorter and easier way with technologies such as artificial intelligence, deep learning and image processing. In this study, a dataset of paper, plastic and food and beverage wastes that are common in the environment was created. In this dataset, paper cups, plastic water bottles and fast food wastes were detected from different locations in nature and photographed. These images were labeled and trained and tested with YoloV3 in deep learning algorithms. In addition, in order to compare the performance of the new dataset, studies were conducted on a global dataset used in the literature. As a result of the studies, it was observed that it was successful in classifying the newly created dataset and the global dataset.

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Section: Introductionmentioning

confidence: 99%