CNN-SSDI: Convolution neural network inspired surveillance system for UAVs detection and identification

Abstract

In recent years, the availability of commercial unmanned air vehicles (UAVs) has increased enormously because of device miniaturization and low cost. However, the abuse of UAVs needs to be investigated to prevent serious security threats for civilians. Therefore, this paper presents a convolutional neural network-based surveillance system for drone detection and its type identification, namely CNN-SSDI. The network architecture is cleverly designed based on deep convolution layers to successfully learn all intrinsic feature maps of radio-frequency signals that are collected from three different types of drones. Further, a detailed comparative analysis of various kernel impairments of the convolution layer structure was investigated under various performance metrics evaluation and higher accuracy in drone surveillance systems. According to the empirical results, CNN-SSDI can detect a UAV with 99.8% accuracy and recognize drone types with an accuracy of 94.5%, which outperforms other existing drone detection and identification techniques.