Machine Learning Analysis of Drone Classification Models: Inspire, Mavic, Phantom, and No Drone
DOI:
https://doi.org/10.48047/myppm523Keywords:
Keywords: Drone classification, Image processing, Predictive modelling, Machine learningAbstract
The proliferation of drone technology has introduced a variety of models, each with distinct functionalities and applications, presenting a challenge in accurately classifying them. Drone technology has become increasingly prevalent across various sectors, including surveillance, agriculture, delivery, and emergency response. As the skies grow more crowded, identifying and classifying drones based on images is becoming critical for airspace security and regulatory compliance. Traditionally, this task has been carried out manuallyby security personnel or air traffic monitors who rely on visual observation
Downloads
Downloads
Published
Issue
Section
License
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
