Providing an Improved Unmanned Aerial Vehicle Detection System to Increase Detection speed Using Deep Learning

In recent years, Unmanned Aerial Vehicles have become significantly available to the public of people. Affordable prices, being equipped with advanced technologies, small sizes, easy portability and … etc. create a lot of worries. For example UAVs can be used for malicious activities, spying from private places, monitoring important locations, carrying dangerous objects such as explosives and etc., which is great threat to society. for this reason, detection and identification is an important work. To solving these challenges, university and industry have present many solutions in recent years. from radar detection systems, video base systems, RF base systems is used to identify and detection UAVs. Based on recent studies, that suggest machine learning-based classification to identify UAVs can be successful. this paper introduces an improved method for detecting UAVs based on deep learning. this system is based on detection by the camera and based on the camera images determine the location of the UAVs on the image and dragging the box around it. This Approach uses the OpenCV library and the YOLO algorithm. images of UAVs are collected and by considering the speed parameter, starting the learning process .after that, The simulation results show that in about 17milliseconds,the UAVs is detected with 85% accuracy.