Summary of Dronevis: Versatile Computer Vision Library For Drones, by Ahmed Heakl et al.
DroneVis: Versatile Computer Vision Library for Drones
by Ahmed Heakl, Fatma Youssef, Victor Parque, Walid Gomaa
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces DroneVis, a novel library that automates computer vision algorithms on Parrot drones. The library offers a versatile set of features and provides various models for diverse computer vision tasks. Implemented in Python, DroneVis adheres to high-quality code standards, allowing for effortless customization and feature expansion according to user requirements. The library also comes with comprehensive documentation, including usage guidelines and illustrative use cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DroneVis is a new tool that helps computers understand what they see on drones. It’s like having a superpower! Imagine being able to automatically recognize objects or track movements from the air. That’s exactly what DroneVis can do. The creators of this library designed it to be easy to use and customize, so you can add your own features and make it work for whatever project you want. There are also instructions and examples to help you get started. |
