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Tian R, Lv Z, Fan Y, Wang T, Sun M, Xu Z. Qualitative classification of waste garments for textile recycling based on machine vision and attention mechanisms. WASTE MANAGEMENT (NEW YORK, N.Y.) 2024; 183:74-86. [PMID: 38728770 DOI: 10.1016/j.wasman.2024.04.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 04/15/2024] [Accepted: 04/22/2024] [Indexed: 05/12/2024]
Abstract
The increasing volume of garment waste underscores the need for advanced sorting and recycling strategies. As a critical procedure in the secondary usage of waste clothes, qualitative classification of garments categorizes post-consumer clothes based on types and styles. However, this process currently relies on manual labor, which is inefficient, labor-intensive, and poses risks to workers. Despite efforts to implement automatic clothes classification systems, challenges persist due to visual complexities such as similar colors, deformations, and occlusions. In response to these challenges, this study introduces an enhanced intelligent machine vision system with attention mechanisms designed to automate the laborious and skill-demanding task of garment classification. Initially, a waste garment dataset comprising approximately 27,000 garments was curated using a self-developed automatic classification platform. Subsequently, the proposed attention method parameters were selected, and a series of benchmarks were conducted against state-of-the-art methods. Finally, the proposed system underwent a two-week online deployment to evaluate its running stability and sensitivity to similar colors, deformation, and occlusion in industrial production settings. The benchmarks indicate that the proposed method significantly improves classification accuracy across various models. The visualization interpretation of Grad-CAM reveals that the proposed method effectively handles complex environments by directing its focus toward garment-related pixels. Notably, the proposed system elevates classification accuracy from 68.28 % to human-level performance (>90 %) while ensuring greater running stability. This advancement holds promise for automating the classification process and potentially alleviating workers from labor-intensive and hazardous tasks associated with clothes recycling.
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Affiliation(s)
- Rui Tian
- School of Chemical and Environmental Engineering, China University of Mining and Technology-Beijing, Beijing 100083, PR China; Inner Mongolia Research Institute, China University of Mining and Technology-Beijing, Ordos 017001, PR China
| | - Ziqi Lv
- School of Chemical and Environmental Engineering, China University of Mining and Technology-Beijing, Beijing 100083, PR China; Inner Mongolia Research Institute, China University of Mining and Technology-Beijing, Ordos 017001, PR China
| | - Yuhan Fan
- School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing 100083, PR China; Inner Mongolia Research Institute, China University of Mining and Technology-Beijing, Ordos 017001, PR China
| | - Tianyu Wang
- Xiaohuanggou Environmental Protection Technology Co., Ltd, Beijing 100020, PR China
| | - Meijie Sun
- School of Chemical and Environmental Engineering, China University of Mining and Technology-Beijing, Beijing 100083, PR China; Inner Mongolia Research Institute, China University of Mining and Technology-Beijing, Ordos 017001, PR China.
| | - Zhiqiang Xu
- School of Chemical and Environmental Engineering, China University of Mining and Technology-Beijing, Beijing 100083, PR China; Inner Mongolia Research Institute, China University of Mining and Technology-Beijing, Ordos 017001, PR China
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Prasad V, Arashpour M. Optimally leveraging depth features to enhance segmentation of recyclables from cluttered construction and demolition waste streams. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 354:120313. [PMID: 38367501 DOI: 10.1016/j.jenvman.2024.120313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/23/2024] [Accepted: 02/06/2024] [Indexed: 02/19/2024]
Abstract
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 the advent of deep learning-based computer vision, this study focuses on improving intelligent identification of valuable recyclables from cluttered and heterogeneous CDW streams in material recovery facilities (MRFs) by optimally leveraging both visual and spatial features (depth). A high-quality CDW RGB-D dataset was curated to capture MRF stream complexities often overlooked in prior studies, and comprises over 3500 images for each modality and more than 160,000 dense object instances of diverse CDW materials with high resource value. In contrast to former studies which directly concatenate RGB and depth features, this study introduces a new depth fusion strategy that utilizes computationally efficient convolutional operations at the end of the conventional waste segmentation architecture to effectively fuse colour and depth information. This avoids cross-modal interference and maximizes the use of distinct information present in the two different modalities. Despite the high clutter and diversity of waste objects, the proposed RGB-DL architecture achieves a 13% increase in segmentation accuracy and a 36% reduction in inference time when compared to the direct concatenation of features. The findings of this study emphasize the benefit of effectively incorporating geometrical features to complement visual cues. This approach helps to deal with the cluttered and varied nature of CDW streams, enhancing automated waste recognition accuracy to improve resource recovery in MRFs. This, in turn, promotes intelligent solid waste management for efficiently managing environmental concerns.
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Affiliation(s)
- Vineet Prasad
- Department of Civil Engineering, Monash University, Melbourne, Australia.
| | - Mehrdad Arashpour
- Department of Civil Engineering, Monash University, Melbourne, Australia.
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Abina A, Puc U, Zidanšek A. Challenges and opportunities of terahertz technology in construction and demolition waste management. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 315:115118. [PMID: 35472828 DOI: 10.1016/j.jenvman.2022.115118] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 04/01/2022] [Accepted: 04/18/2022] [Indexed: 06/14/2023]
Abstract
Construction and demolition waste are one of the largest waste streams generated in the EU by volume. They consist of materials such as concrete, bricks, gypsum, wood, glass, metals, foams, plastics, solvents, asbestos, asphalt, and excavated soil. Nowadays, many of them can be recycled, some even endlessly. This research attempts to contribute to the non-destructive characterization of such a waste with a novel method using terahertz radiation. By combining terahertz imaging and spectroscopy, we performed analytical characterization of selected building materials. The results demonstrate that terahertz technology allows an inside view into some of the non-conducting building materials. THz imaging can detect and visualize the organic solvents in the insulation material, which are often disposed of together with construction and demolition waste. It can also visualize the content of foreign objects or hazardous and toxic substances, which is important for their separation in the recyclate according to the type of the material. Furthermore, THz spectra reveal some spectral lines that can differentiate between different plastics and polymers within the frequency range of 1.0-4.5 THz due to different material structures and chemical compositions. Such results significantly contribute to the decision of which product meets all the standards, which can be returned to the production process due to irregularities or may be disposed of as waste. The only way to reduce construction and demolition waste in the future is to encourage the adoption of innovative technologies like terahertz spectroscopy in combination with traditional methods. This approach can bring some changes also to the construction design philosophy toward more sustainable buildings with minimum end-of-life demolition.
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Affiliation(s)
- Andreja Abina
- Jožef Stefan International Postgraduate School, Jamova cesta 39, SI-1000, Ljubljana, Slovenia.
| | - Uroš Puc
- Jožef Stefan International Postgraduate School, Jamova cesta 39, SI-1000, Ljubljana, Slovenia
| | - Aleksander Zidanšek
- Jožef Stefan International Postgraduate School, Jamova cesta 39, SI-1000, Ljubljana, Slovenia; Department of Condensed Matter Physics, Jozef Stefan Institute, Jamova cesta 39, SI-1000, Ljubljana, Slovenia; Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, SI-2000, Maribor, Slovenia
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Kroell N, Chen X, Greiff K, Feil A. Optical sensors and machine learning algorithms in sensor-based material flow characterization for mechanical recycling processes: A systematic literature review. WASTE MANAGEMENT (NEW YORK, N.Y.) 2022; 149:259-290. [PMID: 35760014 DOI: 10.1016/j.wasman.2022.05.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 04/17/2022] [Accepted: 05/17/2022] [Indexed: 06/15/2023]
Abstract
Digital technologies hold enormous potential for improving the performance of future-generation sorting and processing plants; however, this potential remains largely untapped. Improved sensor-based material flow characterization (SBMC) methods could enable new sensor applications such as adaptive plant control, improved sensor-based sorting (SBS), and more far-reaching data utilizations along the value chain. This review aims to expedite research on SBMC by (i) providing a comprehensive overview of existing SBMC publications, (ii) summarizing existing SBMC methods, and (iii) identifying future research potentials in SBMC. By conducting a systematic literature search covering the period 2000 - 2021, we identified 198 peer-reviewed journal articles on SBMC applications based on optical sensors and machine learning algorithms for dry-mechanical recycling of non-hazardous waste. The review shows that SBMC has received increasing attention in recent years, with more than half of the reviewed publications published between 2019 and 2021. While applications were initially focused solely on SBS, the last decade has seen a trend toward new applications, including sensor-based material flow monitoring, quality control, and process monitoring/control. However, SBMC at the material flow and process level remains largely unexplored, and significant potential exists in upscaling investigations from laboratory to plant scale. Future research will benefit from a broader application of deep learning methods, increased use of low-cost sensors and new sensor technologies, and the use of data streams from existing SBS equipment. These advancements could significantly improve the performance of future-generation sorting and processing plants, keep more materials in closed loops, and help paving the way towards circular economy.
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Affiliation(s)
- Nils Kroell
- Department of Anthropogenic Material Cycles, RWTH Aachen University, Germany.
| | - Xiaozheng Chen
- Department of Anthropogenic Material Cycles, RWTH Aachen University, Germany
| | - Kathrin Greiff
- Department of Anthropogenic Material Cycles, RWTH Aachen University, Germany
| | - Alexander Feil
- Department of Anthropogenic Material Cycles, RWTH Aachen University, Germany
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Chen S, Wang T, Bao Z, Lou V. A Path Analysis of the Effect of Neighborhood Built Environment on Public Health of Older Adults: A Hong Kong Study. Front Public Health 2022; 10:861836. [PMID: 35359794 PMCID: PMC8964032 DOI: 10.3389/fpubh.2022.861836] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 02/16/2022] [Indexed: 11/15/2022] Open
Abstract
Introduction Health deterioration among frail older adults is a public health concern. Among the multi-dimensional factors, the neighborhood built environment is crucial for one's health. Although the relationship between the built environment and health in the general population has been thoroughly investigated, it has been ignored in the case of frail older adults, who may have difficulties in their daily basic living skills. A path analysis is constructed to model the proposed theoretical framework involving the neighborhood built environment and health among frail older adults. This study thus aims to investigate the environmental influences on health, and to validate the theoretical framework proposed for health and social services. Methods This study used secondary data collected in Hong Kong. A sample of 969 older community dwellers aged 60 or above were frail with at least one activity of daily living. Demographic information, neighborhood built environment data, service utilization, and health conditions were collected from these participants and their caregivers. A path analysis was performed to examine the proposed theoretical framework. Results The health condition was of general concern, including frailty and incapacities in daily activities in frail older adults. Besides psychosocial factors, service use, and caregivers' care quality, the built environment had a significant impact on the health of older adults as well. Specifically, more facilities offering services and groceries, a shorter distance to the nearest metro station, and more greenery exposure are associated with a better-expected health condition among frail older adults. Discussion The proposed theoretical framework successfully supplements past negligence on the relationship between the built environment and the health of frail older adults. The findings further imply that policymakers should promote the usability of transit and greenery in neighborhoods and communities. In addition, service utilization should be improved to meet the basic needs of frail older adults in the communities.
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Affiliation(s)
- Shuangzhou Chen
- Department of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong, China.,Sau Po Center on Ageing, The University of Hong Kong, Hong Kong, China
| | - Ting Wang
- Division of Landscape Architecture, Department of Architecture, The University of Hong Kong, Hong Kong, China
| | - Zhikang Bao
- Department of Real Estate and Construction, Faculty of Architecture, The University of Hong Kong, Hong Kong, China
| | - Vivian Lou
- Department of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong, China.,Sau Po Center on Ageing, The University of Hong Kong, Hong Kong, China
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Lu W, Chen J. Computer vision for solid waste sorting: A critical review of academic research. WASTE MANAGEMENT (NEW YORK, N.Y.) 2022; 142:29-43. [PMID: 35172271 DOI: 10.1016/j.wasman.2022.02.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 12/12/2021] [Accepted: 02/06/2022] [Indexed: 06/14/2023]
Abstract
Waste sorting is highly recommended for municipal solid waste (MSW) management. Increasingly, computer vision (CV), robotics, and other smart technologies are used for MSW sorting. Particularly, the field of CV-enabled waste sorting is experiencing an unprecedented explosion of academic research. However, little attention has been paid to understanding its evolvement path, status quo, and prospects and challenges ahead. To address the knowledge gap, this paper provides a critical review of academic research that focuses on CV-enabled MSW sorting. Prevalent CV algorithms, in particular their technical rationales and prediction performance, are introduced and compared. The distribution of academic research outputs is also examined from the aspects of waste sources, task objectives, application domains, and dataset accessibility. The review discovers a trend of shifting from traditional machine learning to deep learning algorithms. The robustness of CV for waste sorting is increasingly enhanced owing to the improved computation powers and algorithms. Academic studies were unevenly distributed in different sectors such as household, commerce and institution, and construction. Too often, researchers reported some preliminary studies using simplified environments and artificially collected data. Future research efforts are encouraged to consider the complexities of real-world scenarios and implement CV in industrial waste sorting practice. This paper also calls for open sharing of waste image datasets for interested researchers to train and evaluate their CV algorithms.
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Affiliation(s)
- Weisheng Lu
- Department of Real Estate and Construction, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Junjie Chen
- Department of Real Estate and Construction, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
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Dong Z, Chen J, Lu W. Computer vision to recognize construction waste compositions: A novel boundary-aware transformer (BAT) model. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 305:114405. [PMID: 34995944 DOI: 10.1016/j.jenvman.2021.114405] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 12/21/2021] [Accepted: 12/27/2021] [Indexed: 06/14/2023]
Abstract
Recognition of construction waste compositions using computer vision (CV) is increasingly explored to enable its subsequent management, e.g., determining chargeable levy at disposal facilities or waste sorting using robot arms. However, the applicability of existing CV-enabled construction waste recognition in real-life scenarios is limited by their relatively low accuracy, characterized by a failure to distinguish boundaries among different waste materials. This paper aims to propose a novel boundary-aware Transformer (BAT) model for fine-grained composition recognition of construction waste mixtures. First, a pre-processing workflow is devised to separate the hard-to-recognize edges from the background. Second, a Transformer structure with a self-designed cascade decoder is developed to segment different waste materials from construction waste mixtures. Finally, a learning-enabled edge refinement scheme is used to fine-tune the ignored boundaries, further boosting the segmentation precision. The performance of the BAT model was evaluated on a benchmark dataset comprising nine types of materials in a cluttered and mixture state. It recorded a 5.48% improvement of MIoU (mean intersection over union) and 3.65% of MAcc (Mean Accuracy) against the baseline. The research contributes to the body of interdisciplinary knowledge by presenting a novel deep learning model for construction waste material semantic segmentation. It can also expedite the applications of CV in construction waste management to achieve a circular economy.
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Affiliation(s)
- Zhiming Dong
- Department of Real Estate and Construction, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
| | - Junjie Chen
- Department of Real Estate and Construction, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
| | - Weisheng Lu
- Department of Real Estate and Construction, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
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Yogarathinam LT, Velswamy K, Gangasalam A, Ismail AF, Goh PS, Narayanan A, Abdullah MS. Performance evaluation of whey flux in dead-end and cross-flow modes via convolutional neural networks. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 301:113872. [PMID: 34607142 DOI: 10.1016/j.jenvman.2021.113872] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 09/08/2021] [Accepted: 09/26/2021] [Indexed: 06/13/2023]
Abstract
Effluent originating from cheese production puts pressure onto environment due to its high organic load. Therefore, the main objective of this work was to compare the influence of different process variables (transmembrane pressure (TMP), Reynolds number and feed pH) on whey protein recovery from synthetic and industrial cheese whey using polyethersulfone (PES 30 kDa) membrane in dead-end and cross-flow modes. Analysis on the fouling mechanistic model indicates that cake layer formation is dominant as compared to other pore blocking phenomena evaluated. Among the input variables, pH of whey protein solution has the biggest influence towards membrane flux and protein rejection performances. At pH 4, electrostatic attraction experienced by whey protein molecules prompted a decline in flux. Cross-flow filtration system exhibited a whey rejection value of 0.97 with an average flux of 69.40 L/m2h and at an experimental condition of 250 kPa and 8 for TMP and pH, respectively. The dynamic behavior of whey effluent flux was modeled using machine learning (ML) tool convolutional neural networks (CNN) and recursive one-step prediction scheme was utilized. Linear and non-linear correlation indicated that CNN model (R2 - 0.99) correlated well with the dynamic flux experimental data. PES 30 kDa membrane displayed a total protein rejection coefficient of 0.96 with 55% of water recovery for the industrial cheese whey effluent. Overall, these filtration studies revealed that this dynamic whey flux data studies using the CNN modeling also has a wider scope as it can be applied in sensor tuning to monitor flux online by means of enhancing whey recovery efficiency.
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Affiliation(s)
- Lukka Thuyavan Yogarathinam
- Membrane Research Laboratory, Department of Chemical Engineering, National Institute of Technology, Tiruchirappalli, 620 015, India; Advanced Membrane Technology Research Centre (AMTEC), Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia
| | - Kirubakaran Velswamy
- Department of Chemical and Materials Engineering, Donadeo Innovation Center for Engineering, University of Alberta-T6G 1H9, Edmonton, Canada
| | - Arthanareeswaran Gangasalam
- Membrane Research Laboratory, Department of Chemical Engineering, National Institute of Technology, Tiruchirappalli, 620 015, India.
| | - Ahmad Fauzi Ismail
- Advanced Membrane Technology Research Centre (AMTEC), Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia.
| | - Pei Sean Goh
- Advanced Membrane Technology Research Centre (AMTEC), Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia
| | - Anantharaman Narayanan
- Membrane Research Laboratory, Department of Chemical Engineering, National Institute of Technology, Tiruchirappalli, 620 015, India
| | - Mohd Sohaimi Abdullah
- Advanced Membrane Technology Research Centre (AMTEC), Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia
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