A survey of smart dustbin systems using the IoT and deep learning DOI Creative Commons
Menaka Pushpa Arthur,

S. Shoba,

A.R. Pandey

et al.

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(3)

Published: Feb. 15, 2024

Abstract With massive population growth and a shift in the urban culture smart cities, constant generation of waste continues to create unsanitary living conditions for city dwellers. Overflowing solid garbage rapid non-degradable produce slew infectious illnesses that proliferate throughout ecosystem. Conventional management systems have proved be increasingly harmful densely populated areas like cities. Also, such require real-time manual monitoring garbage, high labor costs, maintenance. Monitoring on timely basis reducing costs is scarcely possible, realistically, municipal corporation. A Smart Dustbin System (SDS) proposed implemented ensure hygiene. This paper undertakes comprehensive analysis application dustbin systems, following an extensive literature review discussion recent research expected help improve systems. current SDS used with most advances from deep learning, computer vision, Internet Things. The system day-to-day life minimizes overloading bins, lowers saves energy time. It also helps keep cities clean, lowering risk disease transmission. primary users are universities, malls, high-rise buildings. evolution over years various features technologies well analyzed. datasets Waste Management benchmark image presented under AI perception. results existing works compared highlight potential limitations these works.

Language: Английский

Smart waste management: A paradigm shift enabled by artificial intelligence DOI Creative Commons
David B. Olawade, Oluwaseun Fapohunda, Ojima Z. Wada

et al.

Waste Management Bulletin, Journal Year: 2024, Volume and Issue: 2(2), P. 244 - 263

Published: May 9, 2024

Waste management poses a pressing global challenge, necessitating innovative solutions for resource optimization and sustainability. Traditional practices often prove insufficient in addressing the escalating volume of waste its environmental impact. However, advent Artificial Intelligence (AI) technologies offers promising avenues tackling complexities systems. This review provides comprehensive examination AI's role management, encompassing collection, sorting, recycling, monitoring. It delineates potential benefits challenges associated with each application while emphasizing imperative improved data quality, privacy measures, cost-effectiveness, ethical considerations. Furthermore, future prospects AI integration Internet Things (IoT), advancements machine learning, importance collaborative frameworks policy initiatives were discussed. In conclusion, holds significant promise enhancing practices, such as concerns, cost implications is paramount. Through concerted efforts ongoing research endeavors, transformative can be fully harnessed to drive sustainable efficient practices.

Language: Английский

Citations

65
Machine learning in construction and demolition waste management: Progress, challenges, and future directions DOI
Yu Gao,

Jiayuan Wang,

Xiaoxiao Xu

et al.

Automation in Construction, Journal Year: 2024, Volume and Issue: 162, P. 105380 - 105380

Published: March 16, 2024

Language: Английский

Citations

29
Deep learning-based models for environmental management: Recognizing construction, renovation, and demolition waste in-the-wild DOI Creative Commons

Diani Sirimewan,

Milad Bazli, Sudharshan N. Raman

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 351, P. 119908 - 119908

Published: Jan. 1, 2024

The construction industry generates a substantial volume of solid waste, often destinated for landfills, causing significant environmental pollution. Waste recycling is decisive in managing waste yet challenging due to labor-intensive sorting processes and the diverse forms waste. Deep learning (DL) models have made remarkable strides automating domestic recognition sorting. However, application DL recognize derived from construction, renovation, demolition (CRD) activities remains limited context-specific studies conducted previous research. This paper aims realistically capture complexity streams CRD context. study encompasses collecting annotating images real-world, uncontrolled environments. It then evaluates performance state-of-the-art automatically recognizing in-the-wild. Several pre-trained networks are utilized perform effectual feature extraction transfer during model training. results demonstrated that models, whether integrated with larger or lightweight backbone can composition in-the-wild which useful automated outcome emphasized applicability across various industrial domains, thereby contributing resource recovery encouraging management efforts.

Language: Английский

Citations

22
A Reliable and Robust Deep Learning Model for Effective Recyclable Waste Classification DOI Creative Commons
Md. Mosarrof Hossen,

Molla E. Majid,

Saad Bin Abul Kashem

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 13809 - 13821

Published: Jan. 1, 2024

In response to the growing waste problem caused by industrialization and modernization, need for an automated sorting recycling system sustainable management has become ever more pressing. Deep learning made significant advancements in image classification, making it ideally suited applications. This application depends on development of a suitable deep model capable accurately categorizing various categories waste. this study, we present RWC-Net (recyclable classification network), novel designed six distinct using TrashNet dataset 2,527 images The performance our is subjected intensive quantitative qualitative evaluations compared state-of-art techniques. proposed outperformed several state-of-the-art models obtaining remarkable overall accuracy rate 95.01 percent. addition, receives high F1-scores each categories: 97.24% cardboard, 96.18% glass, 94% metal, 95.73% paper, 93.67% plastic, 88.55% litter. reliability demonstrated qualitatively through saliency maps generated Score-CAM (class activation mapping) model, which provide visual insights into its across categories. These results highlight model's demonstrate potential as effective solution.

Language: Английский

Citations

20
Recent Developments in Technology for Sorting Plastic for Recycling: The Emergence of Artificial Intelligence and the Rise of the Robots DOI Creative Commons

Cesar Lubongo,

Mohammed A. A. Bin Daej, Paschalis Alexandridis

et al.

Recycling, Journal Year: 2024, Volume and Issue: 9(4), P. 59 - 59

Published: July 15, 2024

Plastics recycling is an important component of the circular economy. In mechanical recycling, recovery high-quality plastics for subsequent reprocessing requires plastic waste to be first sorted by type, color, and size. chemical certain types should removed as they negatively affect process. Such sortation objects at Materials Recovery Facilities (MRFs) relies increasingly on automated technology. Critical any sorting proper identification type. Spectroscopy used this end, augmented machine learning (ML) artificial intelligence (AI). Recent developments in application ML/AI are highlighted here, state art presented. Commercial equipment recyclables identified from a survey publicly available information. Automated equipment, ML/AI-based sorters, robotic sorters currently market evaluated regarding their sensors, capability sort plastics, primary application, throughput, accuracy. This information reflects rapid progress achieved plastics. However, film, dark comprising multiple polymers remains challenging. Improvements and/or new solutions forthcoming.

Language: Английский

Citations

20
A state-of-the-art review on robotics in waste sorting: scope and challenges DOI

Anushka G. Satav,

Sunidhi Kubade,

Chinmay Amrutkar

et al.

International Journal on Interactive Design and Manufacturing (IJIDeM), Journal Year: 2023, Volume and Issue: 17(6), P. 2789 - 2806

Published: May 6, 2023

Language: Английский

Citations

37
Enablers to computer vision technology for sustainable E-waste management DOI
Himanshu Sharma, Harish Kumar, Sachin Kumar Mangla

et al.

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 412, P. 137396 - 137396

Published: May 5, 2023

Language: Английский

Citations

31
A smart waste classification model using hybrid CNN-LSTM with transfer learning for sustainable environment DOI
Umesh Kumar Lilhore, Sarita Simaiya, Surjeet Dalal

et al.

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(10), P. 29505 - 29529

Published: Sept. 13, 2023

Language: Английский

Citations

25
Revolutionizing construction and demolition waste sorting: Insights from artificial intelligence and robotic applications DOI Creative Commons
Shanuka Dodampegama, Lei Hou, Ehsan Asadi

et al.

Resources Conservation and Recycling, Journal Year: 2023, Volume and Issue: 202, P. 107375 - 107375

Published: Dec. 22, 2023

The growing environmental concerns have emerged the necessity of sustainable waste management construction and demolition (C&D) wastes. This review explores advancements in artificial intelligence (AI) robotics to automate C&D sorting. A comprehensive examination this domain is conducted by structuring paper around six research questions. Current trends potential future directions are revealed performing methodology data analysis involving bibliometric scientometric studies. Notably, recent emphasises circular economy, AI, robotics, underscoring importance enhance AI for precise categorisation. scarcity publicly available datasets a central challenge domain, that hinders effective applications. However, augmentation, synthesis, generative transfer learning been identified as crucial techniques dataset quality categorization accuracy. While draws significant attention shows lack AI-enabled systems due complex nature sorting collection. In summary, study's findings highlight need new methods integrating multisensory fusion, unsupervised machine continuously learn adapt streams materials, making them highly efficient management.

Language: Английский

Citations

24
Optimally leveraging depth features to enhance segmentation of recyclables from cluttered construction and demolition waste streams DOI Creative Commons

Vineet Prasad,

Mehrdad Arashpour

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 354, P. 120313 - 120313

Published: Feb. 16, 2024

This paper addresses the critical environmental issue of effectively managing construction and demolition waste (CDW), which has seen a global surge due to rapid urbanization. With advent deep learning-based computer vision, this study focuses on improving intelligent identification valuable recyclables from cluttered heterogeneous CDW streams in material recovery facilities (MRFs) by optimally leveraging both visual spatial features (depth). A high-quality RGB-D dataset was curated capture MRF stream complexities often overlooked prior studies, comprises over 3500 images for each modality more than 160,000 dense object instances diverse materials with high resource value. In contrast former studies directly concatenate RGB depth features, introduces new fusion strategy that utilizes computationally efficient convolutional operations at end conventional segmentation architecture fuse colour information. avoids cross-modal interference maximizes use distinct information present two different modalities. Despite clutter diversity objects, proposed RGB-DL achieves 13% increase accuracy 36% reduction inference time when compared direct concatenation features. The findings emphasize benefit incorporating geometrical complement cues. approach helps deal varied nature streams, enhancing automated recognition improve MRFs. This, turn, promotes solid management efficiently concerns.

Language: Английский

Citations

10