VBSF-TLD: Validation-Based Approach for Soft Computing-Inspired Transfer Learning in Drone Detection

TL;DR

This paper introduces a transfer learning-based drone detection method using validation techniques to improve accuracy in security-critical environments, leveraging pre-trained models and benchmark datasets.

Contribution

It proposes a novel validation-based transfer learning approach for drone detection, enhancing deep learning performance with limited data in security applications.

Findings

Effective detection on Drone-vs-Bird and UAVDT datasets

High IOU validation accuracy demonstrates robustness

Potential for deployment in high-security zones

Abstract

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.