Drone-type-Set: Drone types detection benchmark for drone detection and tracking
This addresses a national security issue by enabling better drone type detection, but it is incremental as it primarily provides a new dataset and benchmarks existing methods.
The paper tackles the problem of detecting unauthorized drones for security by creating a new dataset of various drone types and comparing object detection models like YOLO versions and Detectronv2 on it, with experimental results showing performance metrics such as mAP scores (e.g., YOLOv5 achieved 0.85 mAP).
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