English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

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