Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection
<p dir="ltr">Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key consideratio...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , , , , , |
| منشور في: |
2024
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إضافة وسم
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| _version_ | 1864513546065281024 |
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| author | Faizul Rakib Sayem (21399890) |
| author2 | Md. Sakib Bin Islam (20494559) Mansura Naznine (21399893) Mohammad Nashbat (17542194) Mazhar Hasan-Zia (21399896) Ali K Ansaruddin Kunju (21399899) Amith Khandakar (14151981) Azad Ashraf (17541924) Molla Ehsanul Majid (21399902) Saad Bin Abul Kashem (17773188) Muhammad E. H. Chowdhury (14150526) |
| author2_role | author author author author author author author author author author |
| author_facet | Faizul Rakib Sayem (21399890) Md. Sakib Bin Islam (20494559) Mansura Naznine (21399893) Mohammad Nashbat (17542194) Mazhar Hasan-Zia (21399896) Ali K Ansaruddin Kunju (21399899) Amith Khandakar (14151981) Azad Ashraf (17541924) Molla Ehsanul Majid (21399902) Saad Bin Abul Kashem (17773188) Muhammad E. H. Chowdhury (14150526) |
| author_role | author |
| dc.creator.none.fl_str_mv | Faizul Rakib Sayem (21399890) Md. Sakib Bin Islam (20494559) Mansura Naznine (21399893) Mohammad Nashbat (17542194) Mazhar Hasan-Zia (21399896) Ali K Ansaruddin Kunju (21399899) Amith Khandakar (14151981) Azad Ashraf (17541924) Molla Ehsanul Majid (21399902) Saad Bin Abul Kashem (17773188) Muhammad E. H. Chowdhury (14150526) |
| dc.date.none.fl_str_mv | 2024-12-23T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s00521-024-10855-2 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Enhancing_waste_sorting_and_recycling_efficiency_robust_deep_learning-based_approach_for_classification_and_detection/29117819 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Environmental engineering Information and computing sciences Artificial intelligence Computer vision and multimedia computation Data management and data science Machine learning Intelligent waste management Sustainable waste processing Urban solid waste Deep learning for waste management Waste image classification Object detection in waste management |
| dc.title.none.fl_str_mv | Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions.</p><h2>Other Information</h2><p dir="ltr">Published in: Neural Computing and Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00521-024-10855-2" target="_blank">https://dx.doi.org/10.1007/s00521-024-10855-2</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_46003e8d90b688cd887a8bcf0e9b57a3 |
| identifier_str_mv | 10.1007/s00521-024-10855-2 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29117819 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detectionFaizul Rakib Sayem (21399890)Md. Sakib Bin Islam (20494559)Mansura Naznine (21399893)Mohammad Nashbat (17542194)Mazhar Hasan-Zia (21399896)Ali K Ansaruddin Kunju (21399899)Amith Khandakar (14151981)Azad Ashraf (17541924)Molla Ehsanul Majid (21399902)Saad Bin Abul Kashem (17773188)Muhammad E. H. Chowdhury (14150526)EngineeringEnvironmental engineeringInformation and computing sciencesArtificial intelligenceComputer vision and multimedia computationData management and data scienceMachine learningIntelligent waste managementSustainable waste processingUrban solid wasteDeep learning for waste managementWaste image classificationObject detection in waste management<p dir="ltr">Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions.</p><h2>Other Information</h2><p dir="ltr">Published in: Neural Computing and Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00521-024-10855-2" target="_blank">https://dx.doi.org/10.1007/s00521-024-10855-2</a></p>2024-12-23T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s00521-024-10855-2https://figshare.com/articles/journal_contribution/Enhancing_waste_sorting_and_recycling_efficiency_robust_deep_learning-based_approach_for_classification_and_detection/29117819CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/291178192024-12-23T03:00:00Z |
| spellingShingle | Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection Faizul Rakib Sayem (21399890) Engineering Environmental engineering Information and computing sciences Artificial intelligence Computer vision and multimedia computation Data management and data science Machine learning Intelligent waste management Sustainable waste processing Urban solid waste Deep learning for waste management Waste image classification Object detection in waste management |
| status_str | publishedVersion |
| title | Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection |
| title_full | Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection |
| title_fullStr | Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection |
| title_full_unstemmed | Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection |
| title_short | Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection |
| title_sort | Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection |
| topic | Engineering Environmental engineering Information and computing sciences Artificial intelligence Computer vision and multimedia computation Data management and data science Machine learning Intelligent waste management Sustainable waste processing Urban solid waste Deep learning for waste management Waste image classification Object detection in waste management |