Drone-type-Set: Drone types detection benchmark for drone detection and tracking
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913353664823296 |
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| author | AlDosari, Kholoud Osman, AIbtisam Elharrouss, Omar AlMaadeed, Somaya Chaari, Mohamed Zied |
| author_facet | AlDosari, Kholoud Osman, AIbtisam Elharrouss, Omar AlMaadeed, Somaya Chaari, Mohamed Zied |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10398 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Drone-type-Set: Drone types detection benchmark for drone detection and tracking AlDosari, Kholoud Osman, AIbtisam Elharrouss, Omar AlMaadeed, Somaya Chaari, Mohamed Zied Computer Vision and Pattern Recognition 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 |
| title | Drone-type-Set: Drone types detection benchmark for drone detection and tracking |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.10398 |