DWSD: Dense waste segmentation dataset
<p dir="ltr">Waste disposal is a global challenge, especially in densely populated areas. Efficient waste segregation is critical for separating recyclable from non-recyclable materials. While developed countries have established and refined effective waste segmentation and recycling...
محفوظ في:
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2025
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513549645119488 |
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| author | Asfak Ali (20690117) |
| author2 | Suvojit Acharjee (14063616) Md. Manarul Sk. (20690120) Salman Z. Alharthi (17541426) Sheli Sinha Chaudhuri (20690123) Adnan Akhunzada (20151648) |
| author2_role | author author author author author |
| author_facet | Asfak Ali (20690117) Suvojit Acharjee (14063616) Md. Manarul Sk. (20690120) Salman Z. Alharthi (17541426) Sheli Sinha Chaudhuri (20690123) Adnan Akhunzada (20151648) |
| author_role | author |
| dc.creator.none.fl_str_mv | Asfak Ali (20690117) Suvojit Acharjee (14063616) Md. Manarul Sk. (20690120) Salman Z. Alharthi (17541426) Sheli Sinha Chaudhuri (20690123) Adnan Akhunzada (20151648) |
| dc.date.none.fl_str_mv | 2025-04-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.dib.2025.111340 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/DWSD_Dense_waste_segmentation_dataset/28369076 |
| 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 Computer vision and multimedia computation Machine learning Classification and segmentation Computer vision Smart cities Waste management |
| dc.title.none.fl_str_mv | DWSD: Dense waste segmentation dataset |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Waste disposal is a global challenge, especially in densely populated areas. Efficient waste segregation is critical for separating recyclable from non-recyclable materials. While developed countries have established and refined effective waste segmentation and recycling systems, our country still uses manual segregation to identify and process recyclable items. This study presents a dataset intended to improve automatic waste segmentation systems. The dataset consists of 784 images that have been manually annotated for waste classification. These images were primarily taken in and around Jadavpur University, including streets, parks, and lawns. Annotations were created with the Labelme program and are available in color annotation formats. The dataset includes 14 waste categories: plastic containers, plastic bottles, thermocol, metal bottles, plastic cardboard, glass, thermocol plates, plastic, paper, plastic cups, paper cups, aluminum foil, cloth, and nylon. The dataset includes a total of 2350 object segments.</p><h2>Other Information:</h2><p dir="ltr">Published in: Data in Brief<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://doi.org/10.1016/j.dib.2025.111340" target="_blank">https://doi.org/10.1016/j.dib.2025.111340</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_da25cb1a56ffdff6f4333eaa5e5b14ac |
| identifier_str_mv | 10.1016/j.dib.2025.111340 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/28369076 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | DWSD: Dense waste segmentation datasetAsfak Ali (20690117)Suvojit Acharjee (14063616)Md. Manarul Sk. (20690120)Salman Z. Alharthi (17541426)Sheli Sinha Chaudhuri (20690123)Adnan Akhunzada (20151648)EngineeringEnvironmental engineeringInformation and computing sciencesComputer vision and multimedia computationMachine learningClassification and segmentationComputer visionSmart citiesWaste management<p dir="ltr">Waste disposal is a global challenge, especially in densely populated areas. Efficient waste segregation is critical for separating recyclable from non-recyclable materials. While developed countries have established and refined effective waste segmentation and recycling systems, our country still uses manual segregation to identify and process recyclable items. This study presents a dataset intended to improve automatic waste segmentation systems. The dataset consists of 784 images that have been manually annotated for waste classification. These images were primarily taken in and around Jadavpur University, including streets, parks, and lawns. Annotations were created with the Labelme program and are available in color annotation formats. The dataset includes 14 waste categories: plastic containers, plastic bottles, thermocol, metal bottles, plastic cardboard, glass, thermocol plates, plastic, paper, plastic cups, paper cups, aluminum foil, cloth, and nylon. The dataset includes a total of 2350 object segments.</p><h2>Other Information:</h2><p dir="ltr">Published in: Data in Brief<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://doi.org/10.1016/j.dib.2025.111340" target="_blank">https://doi.org/10.1016/j.dib.2025.111340</a></p>2025-04-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.dib.2025.111340https://figshare.com/articles/journal_contribution/DWSD_Dense_waste_segmentation_dataset/28369076CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/283690762025-04-01T00:00:00Z |
| spellingShingle | DWSD: Dense waste segmentation dataset Asfak Ali (20690117) Engineering Environmental engineering Information and computing sciences Computer vision and multimedia computation Machine learning Classification and segmentation Computer vision Smart cities Waste management |
| status_str | publishedVersion |
| title | DWSD: Dense waste segmentation dataset |
| title_full | DWSD: Dense waste segmentation dataset |
| title_fullStr | DWSD: Dense waste segmentation dataset |
| title_full_unstemmed | DWSD: Dense waste segmentation dataset |
| title_short | DWSD: Dense waste segmentation dataset |
| title_sort | DWSD: Dense waste segmentation dataset |
| topic | Engineering Environmental engineering Information and computing sciences Computer vision and multimedia computation Machine learning Classification and segmentation Computer vision Smart cities Waste management |