Drone Detection and Drone Type Classification Using Deep Learning Technique

The demand and usage of drones are increasing day by day, and with this increased popularity, new types of drones are coming into the market for various purposes. However, due to the inflation in the usage of drones, their security has become a major concern. As a result, it is required for drone users to be well aware of drones surrounding them. To address this issue, a complete drone detection and classification scheme has been proposed that depends on deep learning. The scheme involves two parts: drone detection using YOLOV2 and drone classification using Convolutional Neural Networks (CNN). In the existing method, Support Vector Machine (SVM) is used to categorize the drone type as Tricopter or Quadcopter. The results of the proposed deep learning and Artificial Intelligence technology executed in MATLAB are promising. It's worth noting that the detection and classification of drones using deep learning algorithms is an active area of research. There are several other approaches that have been proposed in recent years, such as using radio frequency compressed signals to detect and classify UAVs 1. Another study proposed a hybrid deep learning model that combines LSTM and Bi-GRU classifiers to provide enhanced object detection in drone imagery for search and rescue operations 2. These approaches show great potential for improving the accuracy and efficiency of drone detection and classification.