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المؤلفAlDosari, Khloud
المؤلفOsman, AIbtisam
المؤلفElharrouss, Omar
المؤلفAl-Maadeed, Somaya
المؤلفChaari, Mohamed Zied
تاريخ الإتاحة2024-10-10T11:23:20Z
تاريخ النشر2024-01-01
اسم المنشور2024 International Conference on Intelligent Systems and Computer Vision, ISCV 2024
المعرّفhttp://dx.doi.org/10.1109/ISCV60512.2024.10620104
الاقتباسAlDosari, K., Osman, A., Elharrouss, O., Al-Maadeed, S., & Chaari, M. Z. (2024, May). Drone-type-Set: Drone types detection benchmark for drone detection and tracking. In 2024 International Conference on Intelligent Systems and Computer Vision (ISCV) (pp. 1-7). IEEE.‏
الترقيم الدولي الموحد للكتاب [9798350350180]
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85202342904&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/60024
الملخصThe 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.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعDeep Learning
Detectronv2
Drone Detection
YOLOV3
YOLOV4
YOLOV5
العنوانDrone-type-Set: Drone types detection benchmark for drone detection and tracking
النوعConference Paper
الصفحات1-7
dc.accessType Open Access


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