  With the increasing utilization of Internet of Things (IoT) enabled drones in
diverse applications like photography, delivery, and surveillance, concerns
regarding privacy and security have become more prominent. Drones have the
ability to capture sensitive information, compromise privacy, and pose security
risks. As a result, the demand for advanced technology to automate drone
detection has become crucial. This paper presents a project on a transfer-based
drone detection scheme, which forms an integral part of a computer vision-based
module and leverages transfer learning to enhance performance. By harnessing
the knowledge of pre-trained models from a related domain, transfer learning
enables improved results even with limited training data. To evaluate the
scheme's performance, we conducted tests on benchmark datasets, including the
Drone-vs-Bird Dataset and the UAVDT dataset. Notably, the scheme's
effectiveness is highlighted by its IOU-based validation results, demonstrating
the potential of deep learning-based technology in automating drone detection
in critical areas such as airports, military bases, and other high-security
zones.
