An Ensemble-Based IoT-Enabled Drones Detection Scheme for a Safe Community

Abstract

With the increasing use of Internet of Things (IoT)enabled drones for various purposes, including photography, delivery, and surveillance, concerns related to privacy and security have arisen. Drones have the potential to capture sensitive information, invade privacy, and cause security breaches. Therefore, the need for advanced technology for the automated detection of drones has become crucial. In this paper, we propose an ensemble-based IoT-enabled drones detection scheme (in short, EDDSBS). The presented model is part of a computer visionbased module and uses transfer learning for improved performance. Transfer learning allows the reuse of pre-trained models and their knowledge in a different but related domain, enabling better performance with less training data. To evaluate the performance of the proposed EDDSBS, we test it on benchmark datasets, including the Drone-vs-Bird Dataset and the UAVDT dataset. The proposed EDDSBS outperforms the existing schemes of drone detection (i.e., in terms of accuracy). The results of the presented scheme demonstrate the potential of deep learningbased technology for automated drone detection in critical areas, such as airports, military bases, and other high-security areas. Thus the paper introduces a comprehensive process methodology for drone detection that can be applied in real-world settings for a sustainable and secure environment, which is required for a safe community.

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

“…The experimental results showed that the proposed cost-effective system can perform real-time drone detection with zero false negative (FN) outcomes. An ensemble-based IoT-enabled drone detection approach using transfer learning and background subtraction was proposed in [ 87 ]. The ensemble model includes two DL models: YOLOv4 and ResNet-50.…”