Drone Detection from Video Streams Using Image Processing Techniques and YOLOv7

Drone Detection from Video Streams Using Image Processing Techniques and YOLOv7

Автор: Muhammad K. Kabir, Anika N. Binte Kabir, Jahid H. Rony, Jia Uddin

Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp

Статья в выпуске: 2 vol.16, 2024 года.

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For ensuring the safety issues, a country should establish a secure monitoring system around the most important places. Due to the huge development in unmanned aerial vehicles (UAV), drone detection is a vital part of the safety monitoring system for reducing threats from neighboring countries or terrorist groups. This paper presents a deep learning-based drone detection method. A You Only Look Once (YOLO) v7 architecture is used to train on the dataset. The training dataset consists of drone images in various environments. The trained model was tested on multiple videos of drones from YouTube. Experimental results demonstrate that the model exhibited a recall of 0.9656 and a precision of 0.9509. In addition, the performance of the model compares with the state-of-art models with YOLOv8, YOLO-NAS, Faster-RCNN architectures and it outperforms the other models by maintaining a more stable precision and recall curve.

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Unmanned aerial vehicles (UAV), automatic drone detection, image annotation, You Only Look Once (YOLO)

Короткий адрес: https://sciup.org/15019445

IDR: 15019445   |   DOI: 10.5815/ijigsp.2024.02.07

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