Daily Arxiv

This is a page that curates AI-related papers published worldwide.
All content here is summarized using Google Gemini and operated on a non-profit basis.
Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.

15,500 Seconds: Lean UAV Classification Leveraging PEFT and Pre-Trained Networks

Created by
  • Haebom

Author

Andrew P. Berg, Qian Zhang, Mia Y. Wang

Outline

This paper addresses the growing security concerns posed by unmanned aerial vehicles (UAVs) for consumer and military use. In particular, we extend previous work by focusing on the data shortage problem required for deep learning in UAV audio classification. By leveraging novel approaches such as parameter-efficient fine-tuning, data augmentation, and pre-trained networks, we achieve over 95% validation accuracy using EfficientNet-B0.

Takeaways, Limitations

•
Takeaways:
◦
Presenting an effective method to solve the data shortage problem in UAV audio classification (parameter-efficient fine-tuning, data augmentation, utilizing pre-trained networks)
◦
Demonstrating the practicality of UAV audio classification by achieving high accuracy (over 95%)
•
Limitations:
◦
Lack of detailed information in the paper, such as specific datasets, data augmentation techniques, and types of pre-trained networks.
◦
Lack of performance validation in real environments
◦
Generalization performance to other types of UAV audio or more complex environments is unknown.
👍
Made with Slashpage