Amodal_Fruit_Sizing
We provide a deep-learning method to better estimate the size of partially occluded apples. The method is based on ORCNN (https://github.com/waiyulam/ORCNN) and sizecnn (https://git.wur.nl/blok012/sizecnn), which extended Mask R-CNN network to simultaneously perform modal and amodal instance segment...
| Autores: | , , , , , , , , |
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| Tipo de recurso: | conjunto de datos |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Consorci de Serveis Universitaris de Catalunya (CSUC) |
| Repositorio: | CORA.Repositori de Dades de Recerca |
| OAI Identifier: | oai:dnet:cora.rdr____::47f4d07a87e5b0f33aeb421d778cc955 |
| Acceso en línea: | https://doi.org/10.34810/DATA2315 |
| Access Level: | acceso abierto |
| Palabra clave: | Agricultural Sciences Engineering Precision agriculture Fruit size Horticulture Remote sensing |
| Sumario: | We provide a deep-learning method to better estimate the size of partially occluded apples. The method is based on ORCNN (https://github.com/waiyulam/ORCNN) and sizecnn (https://git.wur.nl/blok012/sizecnn), which extended Mask R-CNN network to simultaneously perform modal and amodal instance segmentation. The amodal mask is used to estimate the fruit diameter in pixels, while the modal mask is used to measure in the depth map the distance between the detected fruit and the camera and calculate the fruit diameter in mm by applying the pinhole camera model. |
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