Detection of Unauthorized Unmanned Aerial Vehicles Using YOLOv5 and Transfer Learning
Al-Qubaydhi N. Alenezi A. Alanazi T. Senyor A. Alanezi N. Alotaibi B. Alotaibi M. Razaque A. Abdelhamid A.A. Alotaibi A.
September 2022MDPI
Electronics (Switzerland)
2022#11Issue 17
Drones/unmanned aerial vehicles (UAVs) have recently grown in popularity due to their inexpensive cost and widespread commercial use. The increased use of drones raises the possibility that they may be employed in illicit activities such as drug smuggling and terrorism. Thus, drone monitoring and automated detection are critical for protecting restricted areas or special zones from illicit drone operations. One of the most challenging difficulties in drone detection in surveillance videos is the apparent likeness of drones against varied backdrops. This paper introduces an automated image-based drone-detection system that uses an enhanced deep-learning-based object-detection algorithm known as you only look once (YOLOv5) to defend restricted territories or special zones from unauthorized drone incursions. The transfer learning to pretrain the model is employed for improving performance due to an insufficient number of samples in our dataset. Furthermore, the model can recognize the detected object in the images and mark the object’s bounding box by joining the results across the region. The experiments show outstanding results for the loss value, drone location detection, precision and recall.
deep learning , drone detection , unmanned aerial vehicle , YOLOv5
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Department of Information Technology, University of Tabuk, Tabuk, 71491, Saudi Arabia
Sensor Networks and Cellular Systems Research Center, University of Tabuk, Tabuk, 71491, Saudi Arabia
Dahaa Research Group, Department of Computer Science, Shaqra University, Shaqra, 11961, Saudi Arabia
Department of Cybersecurity, International Information Technology University, Almaty, 050000, Kazakhstan
Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt
Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
Department of Information Technology
Sensor Networks and Cellular Systems Research Center
Dahaa Research Group
Department of Cybersecurity
Computer Science Department
Department of Computer Science
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