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UAV detection using Deep Learning
(Department of Computer Science & Engineering, NITS, 2022) Barman, Partha Pratim
This project proposes a technique for improving UAV detection using deep learning models. Nowadays, Unmanned Aerial vehicles (Rotary Wing Unmanned Aerial Vehicles), also known as drones, are widely used for various purposes. It is widely used by the entertainment industry, military operations, surveillance, agriculture, etc. Adding explosives to unmanned aerial vehicles can simply be turned into a lethal weapon. The UAV industry is growing at a breakneck pace and is now reachable to common citizens at a significantly lower price. Many countries are investing in drones to use them as military weapons. As Unmanned Aerial vehicles are small and have a small electromagnetic signature, it is difficult for conventional radar to detect. So using object detection, we can detect drones in real-time. This thesis proposes modifying the Yolov5n object detection model, which can detect drones in real-time videos or still images. Precision, Recall, F1 score and Mean average precision are used as evaluation metrics. The proposed model is trained and tested on a UAV dataset from Kaggle.