Intelligent Trash Bin (ITB) with Trash Collection Efficiency Optimization using IoT Sensing

Sohail, Hamza; Ullah, Saif; Khan, Asim Ali; Samin, Omar Bin; Omar, Maryam

doi:10.1109/icict47744.2019.9001982

Cited by 16 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The combination of artificial intelligence (AI)-specifically machine learning/deep learning-and the IoT is an attractive solution for building a garbage classification system [21][22][23]. However, such systems are missing in the extant literature and many studies have failed to consider specific system needs and ensure wide applicability of the systems.…”

Section: Related Workmentioning

confidence: 99%

A Deep Trash Classification Model on Raspberry Pi 4

Tran¹,

Huynh²,

Le³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

Environmental pollution has had substantial impacts on human life, and trash is one of the main sources of such pollution in most countries. Trash classification from a collection of trash images can limit the overloading of garbage disposal systems and efficiently promote recycling activities; thus, development of such a classification system is topical and urgent. This paper proposed an effective trash classification system that relies on a classification module embedded in a hard-ware setup to classify trash in real time. An image dataset is first augmented to enhance the images before classifying them as either inorganic or organic trash. The deep learning-based ResNet-50 model, an improved version of the ResNet model, is used to classify trash from the dataset of trash images. The experimental results, which are tested both on the dataset and in real time, show that ResNet-50 had an average accuracy of 96%, higher than that of related models. Moreover, integrating the classification module into a Raspberry Pi computer, which controlled the trash bin slide so that garbage fell into the appropriate bin for inorganic or organic waste, created a complete trash classification system. This proves the efficiency and high applicability of the proposed system.

show abstract

Section: Related Workmentioning

confidence: 99%

A Deep Trash Classification Model on Raspberry Pi 4

Tran¹,

Huynh²,

Le³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…• Smart trash bin Smart trash bins have been developed with the help of the recent available digital technologies especially IoT and its associated cloud computing and data analytics. The main functions which are currently developed and incorporated in smart bin technologies are summarized in Figure 11.18 (Angin et al, 2018;Balamurugan et al, 2017;Huh et al, 2021;Kumar et al, 2017;Noiki et al, 2021;Sohail et al, 2019;Wahab et al, 2014). Three most notable features of smart bins are waste monitoring, communication and waste segregation (Soni & Kandasamy, 2018).…”

Section: • Processes and Service Layermentioning

confidence: 99%

Marine Plastics Abatement

2023

View full text Add to dashboard Cite

Marine Plastics Abatement Volume 2 focusses on abatement strategies and up-to-date technological innovations against marine plastic pollution such as resource recovery, plastics-to-values, co-processing, dumpsite recovery, etc. With an understanding of technological solutions and proper management practices, this volume suggests ways to develop businesses from plastic wastes with several cases from developed and developing countries. Furthermore, business case studies are presented along with recent scientific information, and practical exercises together with discussions on future trends such as introduction of biodegradable, or decomposable plastics; product designs for recycling/upcycling; etc. ISBN: 9781789063431 (print) ISBN: 9781789063448 (eBook) ISBN: 9781789063448 (ePUB)

show abstract