DroneVis: Versatile Computer Vision Library for Drones
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910467745644544 |
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| author | Heakl, Ahmed Youssef, Fatma Parque, Victor Gomaa, Walid |
| author_facet | Heakl, Ahmed Youssef, Fatma Parque, Victor Gomaa, Walid |
| contents | This paper introduces DroneVis, a novel library designed to automate computer vision algorithms on Parrot drones. DroneVis offers a versatile set of features and provides a diverse range of computer vision tasks along with a variety of models to choose from. Implemented in Python, the library adheres to high-quality code standards, facilitating effortless customization and feature expansion according to user requirements. In addition, comprehensive documentation is provided, encompassing usage guidelines and illustrative use cases. Our documentation, code, and examples are available in https://github.com/ahmedheakl/drone-vis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_00447 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DroneVis: Versatile Computer Vision Library for Drones Heakl, Ahmed Youssef, Fatma Parque, Victor Gomaa, Walid Computer Vision and Pattern Recognition Artificial Intelligence Computers and Society Machine Learning Robotics This paper introduces DroneVis, a novel library designed to automate computer vision algorithms on Parrot drones. DroneVis offers a versatile set of features and provides a diverse range of computer vision tasks along with a variety of models to choose from. Implemented in Python, the library adheres to high-quality code standards, facilitating effortless customization and feature expansion according to user requirements. In addition, comprehensive documentation is provided, encompassing usage guidelines and illustrative use cases. Our documentation, code, and examples are available in https://github.com/ahmedheakl/drone-vis. |
| title | DroneVis: Versatile Computer Vision Library for Drones |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computers and Society Machine Learning Robotics |
| url | https://arxiv.org/abs/2406.00447 |