DWSD: Dense waste segmentation datasetMendeley Data
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 u...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925000721 |
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| author | Asfak Ali Suvojit Acharjee Md. Manarul Sk. Salman Z. Alharthi Sheli Sinha Chaudhuri Adnan Akhunzada |
| author_facet | Asfak Ali Suvojit Acharjee Md. Manarul Sk. Salman Z. Alharthi Sheli Sinha Chaudhuri Adnan Akhunzada |
| author_sort | Asfak Ali |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5ac401abfaf84458887eecde0a6b8108 |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-5ac401abfaf84458887eecde0a6b81082025-02-08T05:00:34ZengElsevierData in Brief2352-34092025-04-0159111340DWSD: Dense waste segmentation datasetMendeley DataAsfak Ali0Suvojit Acharjee1Md. Manarul Sk.2Salman Z. Alharthi3Sheli Sinha Chaudhuri4Adnan Akhunzada5Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700032, India; Corresponding authors.Department of Electronics and Communication Engineering, Narula Institute of Technology, Kolkata, India; Corresponding authors.Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700032, IndiaDepartment of Software Engineering, College of Computing, Umm AL-Qura University, Mecca 24381, Kingdom of Saudi ArabiaDepartment of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700032, IndiaCollege of Computing and Information Technology, University of Doha for Science and Technology, Doha 24449, QatarWaste 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.http://www.sciencedirect.com/science/article/pii/S2352340925000721Classification and segmentationComputer visionSmart citiesWaste management |
| spellingShingle | Asfak Ali Suvojit Acharjee Md. Manarul Sk. Salman Z. Alharthi Sheli Sinha Chaudhuri Adnan Akhunzada DWSD: Dense waste segmentation datasetMendeley Data Data in Brief Classification and segmentation Computer vision Smart cities Waste management |
| title | DWSD: Dense waste segmentation datasetMendeley Data |
| title_full | DWSD: Dense waste segmentation datasetMendeley Data |
| title_fullStr | DWSD: Dense waste segmentation datasetMendeley Data |
| title_full_unstemmed | DWSD: Dense waste segmentation datasetMendeley Data |
| title_short | DWSD: Dense waste segmentation datasetMendeley Data |
| title_sort | dwsd dense waste segmentation datasetmendeley data |
| topic | Classification and segmentation Computer vision Smart cities Waste management |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925000721 |
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