A synthetic shadow dataset of agricultural settings
Shadow, a natural phenomenon resulting from the absence of direct lighting, finds diverse real-world applications beyond computer vision, such as studying its effect on photosynthesis in plants and on the reduction of solar energy harvesting through photovoltaic panels. This article presents a datas...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-06-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340924003330 |
| _version_ | 1797224305363255296 |
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| author | Mengchen Huang Ginés García-Mateos Ruben Fernandez-Beltran |
| author_facet | Mengchen Huang Ginés García-Mateos Ruben Fernandez-Beltran |
| author_sort | Mengchen Huang |
| collection | DOAJ |
| description | Shadow, a natural phenomenon resulting from the absence of direct lighting, finds diverse real-world applications beyond computer vision, such as studying its effect on photosynthesis in plants and on the reduction of solar energy harvesting through photovoltaic panels. This article presents a dataset comprising 50,000 pairs of photorealistic computer-rendered images along with their corresponding physics-based shadow masks, primarily focused on agricultural settings with human activity in the field. The images are generated by simulating a scene in 3D modeling software to produce a pair of top-down images, consisting of a regular image and an overexposed image achieved by adjusting lighting parameters. Specifically, the strength of the light source representing the sun is increased, and all indirect lighting, including global illumination and light bouncing, is disabled. The resulting overexposed image is later converted into a physically accurate shadow mask with minimal annotation errors through post-processing techniques. This dataset holds promise for future research, serving as a basis for transfer learning or as a benchmark for model evaluation in the realm of shadow-related applications such as shadow detection and removal. |
| first_indexed | 2024-04-24T13:51:00Z |
| format | Article |
| id | doaj.art-f535cc5eca8546c3879d64e0eaf813b0 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-04-24T13:51:00Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-f535cc5eca8546c3879d64e0eaf813b02024-04-04T05:04:12ZengElsevierData in Brief2352-34092024-06-0154110364A synthetic shadow dataset of agricultural settingsMengchen Huang0Ginés García-Mateos1Ruben Fernandez-Beltran2Corresponding author.; Department of Computer Science and Systems, University of Murcia, 30100 Murcia, SpainDepartment of Computer Science and Systems, University of Murcia, 30100 Murcia, SpainDepartment of Computer Science and Systems, University of Murcia, 30100 Murcia, SpainShadow, a natural phenomenon resulting from the absence of direct lighting, finds diverse real-world applications beyond computer vision, such as studying its effect on photosynthesis in plants and on the reduction of solar energy harvesting through photovoltaic panels. This article presents a dataset comprising 50,000 pairs of photorealistic computer-rendered images along with their corresponding physics-based shadow masks, primarily focused on agricultural settings with human activity in the field. The images are generated by simulating a scene in 3D modeling software to produce a pair of top-down images, consisting of a regular image and an overexposed image achieved by adjusting lighting parameters. Specifically, the strength of the light source representing the sun is increased, and all indirect lighting, including global illumination and light bouncing, is disabled. The resulting overexposed image is later converted into a physically accurate shadow mask with minimal annotation errors through post-processing techniques. This dataset holds promise for future research, serving as a basis for transfer learning or as a benchmark for model evaluation in the realm of shadow-related applications such as shadow detection and removal.http://www.sciencedirect.com/science/article/pii/S2352340924003330Rendered shadow imagesComputer visionShadow detectionDeep learningAgrovoltaic systemsBlender |
| spellingShingle | Mengchen Huang Ginés García-Mateos Ruben Fernandez-Beltran A synthetic shadow dataset of agricultural settings Data in Brief Rendered shadow images Computer vision Shadow detection Deep learning Agrovoltaic systems Blender |
| title | A synthetic shadow dataset of agricultural settings |
| title_full | A synthetic shadow dataset of agricultural settings |
| title_fullStr | A synthetic shadow dataset of agricultural settings |
| title_full_unstemmed | A synthetic shadow dataset of agricultural settings |
| title_short | A synthetic shadow dataset of agricultural settings |
| title_sort | synthetic shadow dataset of agricultural settings |
| topic | Rendered shadow images Computer vision Shadow detection Deep learning Agrovoltaic systems Blender |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924003330 |
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