  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
