Real-Time Vehicles Detection Using a Tello Drone with YOLOv5 Algorithm
Abstract
Incorporating Unmanned Aerial Vehicles (UAVs) within Artificial Intelligence systems has given rise to an essential and academically significant approach in the domain of vehicle detection. This study introduces a real-time vehicle detection framework leveraging the you only look once (YOLO) algorithm for precise identification of vehicles, employing the camera on the DJI Tello drone. The research is underpinned by a rich dataset encompassing approximately 2000 images, meticulously annotated with the respective vehicle angles. The framework’s innovation lies in its comprehensive training regimen, encompassing all angles of vehicles: Vehicle-front, vehicle-rear, vehicle-above, and vehicle-sides. This holistic approach aims to yield a model capable of accurately identifying and tracking vehicles from a multitude of viewing angles. The choice of the YOLO algorithm, further enhanced by Ultralytics HUB, ensures the robustness and accuracy required for the detection of moving objects. The model’s capability to effectively track objects is a testament to the algorithm’s efficacy. In assessing the framework’s performance, we employed a comprehensive set of evaluation parameters, including mean average precision, precision, recall, and F1 score. This research not only underscores the practicality of UAVs in the field of artificial intelligence but also highlights the excellence achieved in real-time vehicle detection.
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References
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