Drone-type-Set: Drone types detection benchmark for drone detection and tracking

TL;DR

This paper introduces a new drone dataset and compares various object detection models, including YOLO and Detectron2, to improve drone detection and classification for security applications.

Contribution

It provides a comprehensive drone dataset and evaluates multiple detection models, addressing the lack of drone type datasets for AI-based detection.

Findings

YOLOv5 achieved the highest detection accuracy.

Detectron2 outperformed some YOLO versions in certain metrics.

The dataset enables better drone type recognition in AI systems.

Abstract

The Unmanned Aerial Vehicles (UAVs) market has been significantly growing and Considering the availability of drones at low-cost prices the possibility of misusing them, for illegal purposes such as drug trafficking, spying, and terrorist attacks posing high risks to national security, is rising. Therefore, detecting and tracking unauthorized drones to prevent future attacks that threaten lives, facilities, and security, become a necessity. Drone detection can be performed using different sensors, while image-based detection is one of them due to the development of artificial intelligence techniques. However, knowing unauthorized drone types is one of the challenges due to the lack of drone types datasets. For that, in this paper, we provide a dataset of various drones as well as a comparison of recognized object detection models on the proposed dataset including YOLO algorithms with their different versions, like, v3, v4, and v5 along with the Detectronv2. The experimental results of different models are provided along with a description of each method. The collected dataset can be found in https://drive.google.com/drive/folders/1EPOpqlF4vG7hp4MYnfAecVOsdQ2JwBEd?usp=share_link