A Reliable and Robust Deep Learning Model for Effective Recyclable Waste Classification
<p dir="ltr">In response to the growing waste problem caused by industrialization and modernization, the need for an automated waste sorting and recycling system for sustainable waste management has become ever more pressing. Deep learning has made significant advancements in image c...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , , , , |
| منشور في: |
2024
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513543597981696 |
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| author | Md. Mosarrof Hossen (21401603) |
| author2 | Molla E. Majid (21323921) Saad Bin Abul Kashem (17773188) Amith Khandakar (14151981) Mohammad Nashbat (17542194) Azad Ashraf (17541924) Mazhar Hasan-Zia (21399896) Ali K. Ansaruddin Kunju (21399059) Saidul Kabir (15302407) Muhammad E. H. Chowdhury (14150526) |
| author2_role | author author author author author author author author author |
| author_facet | Md. Mosarrof Hossen (21401603) Molla E. Majid (21323921) Saad Bin Abul Kashem (17773188) Amith Khandakar (14151981) Mohammad Nashbat (17542194) Azad Ashraf (17541924) Mazhar Hasan-Zia (21399896) Ali K. Ansaruddin Kunju (21399059) Saidul Kabir (15302407) Muhammad E. H. Chowdhury (14150526) |
| author_role | author |
| dc.creator.none.fl_str_mv | Md. Mosarrof Hossen (21401603) Molla E. Majid (21323921) Saad Bin Abul Kashem (17773188) Amith Khandakar (14151981) Mohammad Nashbat (17542194) Azad Ashraf (17541924) Mazhar Hasan-Zia (21399896) Ali K. Ansaruddin Kunju (21399059) Saidul Kabir (15302407) Muhammad E. H. Chowdhury (14150526) |
| dc.date.none.fl_str_mv | 2024-01-17T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2024.3354774 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Reliable_and_Robust_Deep_Learning_Model_for_Effective_Recyclable_Waste_Classification/29605163 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Environmental engineering Environmental sciences Environmental management Pollution and contamination Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Waste management Recycling Waste classification Multi-label classification Convolutional neural network (CNN) Deep learning |
| dc.title.none.fl_str_mv | A Reliable and Robust Deep Learning Model for Effective Recyclable Waste Classification |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">In response to the growing waste problem caused by industrialization and modernization, the need for an automated waste sorting and recycling system for sustainable waste management has become ever more pressing. Deep learning has made significant advancements in image classification, making it ideally suited for waste sorting applications. This application depends on the development of a suitable deep learning model capable of accurately categorizing various categories of waste. In this study, we present RWC-Net (recyclable waste classification network), a novel deep learning model designed for the classification of six distinct waste categories using the TrashNet dataset of 2,527 images of waste. The performance of our model is subjected to intensive quantitative and qualitative evaluations and is compared to various state-of-art waste classification techniques. The proposed model outperformed several state-of-the-art models by obtaining a remarkable overall accuracy rate of 95.01 percent. In addition, it receives high F1-scores for each of the six waste categories: 97.24% for cardboard, 96.18% for glass, 94% for metal, 95.73% for paper, 93.67% for plastic, and 88.55% for litter. The reliability of the model is demonstrated qualitatively through the saliency maps generated by Score-CAM (class activation mapping) model, which provide visual insights into its performance across various waste categories. These results highlight the model’s accuracy and demonstrate its potential as an effective automated waste classification and management solution.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3354774" target="_blank">https://dx.doi.org/10.1109/access.2024.3354774</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_e545b651a3bccde58b2aec557a3bb419 |
| identifier_str_mv | 10.1109/access.2024.3354774 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29605163 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Reliable and Robust Deep Learning Model for Effective Recyclable Waste ClassificationMd. Mosarrof Hossen (21401603)Molla E. Majid (21323921)Saad Bin Abul Kashem (17773188)Amith Khandakar (14151981)Mohammad Nashbat (17542194)Azad Ashraf (17541924)Mazhar Hasan-Zia (21399896)Ali K. Ansaruddin Kunju (21399059)Saidul Kabir (15302407)Muhammad E. H. Chowdhury (14150526)EngineeringEnvironmental engineeringEnvironmental sciencesEnvironmental managementPollution and contaminationInformation and computing sciencesArtificial intelligenceComputer vision and multimedia computationMachine learningWaste managementRecyclingWaste classificationMulti-label classificationConvolutional neural network (CNN)Deep learning<p dir="ltr">In response to the growing waste problem caused by industrialization and modernization, the need for an automated waste sorting and recycling system for sustainable waste management has become ever more pressing. Deep learning has made significant advancements in image classification, making it ideally suited for waste sorting applications. This application depends on the development of a suitable deep learning model capable of accurately categorizing various categories of waste. In this study, we present RWC-Net (recyclable waste classification network), a novel deep learning model designed for the classification of six distinct waste categories using the TrashNet dataset of 2,527 images of waste. The performance of our model is subjected to intensive quantitative and qualitative evaluations and is compared to various state-of-art waste classification techniques. The proposed model outperformed several state-of-the-art models by obtaining a remarkable overall accuracy rate of 95.01 percent. In addition, it receives high F1-scores for each of the six waste categories: 97.24% for cardboard, 96.18% for glass, 94% for metal, 95.73% for paper, 93.67% for plastic, and 88.55% for litter. The reliability of the model is demonstrated qualitatively through the saliency maps generated by Score-CAM (class activation mapping) model, which provide visual insights into its performance across various waste categories. These results highlight the model’s accuracy and demonstrate its potential as an effective automated waste classification and management solution.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3354774" target="_blank">https://dx.doi.org/10.1109/access.2024.3354774</a></p>2024-01-17T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2024.3354774https://figshare.com/articles/journal_contribution/A_Reliable_and_Robust_Deep_Learning_Model_for_Effective_Recyclable_Waste_Classification/29605163CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/296051632024-01-17T03:00:00Z |
| spellingShingle | A Reliable and Robust Deep Learning Model for Effective Recyclable Waste Classification Md. Mosarrof Hossen (21401603) Engineering Environmental engineering Environmental sciences Environmental management Pollution and contamination Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Waste management Recycling Waste classification Multi-label classification Convolutional neural network (CNN) Deep learning |
| status_str | publishedVersion |
| title | A Reliable and Robust Deep Learning Model for Effective Recyclable Waste Classification |
| title_full | A Reliable and Robust Deep Learning Model for Effective Recyclable Waste Classification |
| title_fullStr | A Reliable and Robust Deep Learning Model for Effective Recyclable Waste Classification |
| title_full_unstemmed | A Reliable and Robust Deep Learning Model for Effective Recyclable Waste Classification |
| title_short | A Reliable and Robust Deep Learning Model for Effective Recyclable Waste Classification |
| title_sort | A Reliable and Robust Deep Learning Model for Effective Recyclable Waste Classification |
| topic | Engineering Environmental engineering Environmental sciences Environmental management Pollution and contamination Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Waste management Recycling Waste classification Multi-label classification Convolutional neural network (CNN) Deep learning |