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

To detect unmanned aerial vehicles (UAVs) in real-time, computer vision anddeep learning approaches are evolving research areas. Interest in this problemhas grown due to concerns regarding the possible hazards and misuse ofemploying UAVs in many applications. These include potential privacyviolations. To address the concerns, vision-based object detection methods havebeen developed for UAV detection. However, UAV detection in images with complexbackgrounds and weather artifacts like rain has yet to be reasonably studied.Hence, for this purpose, we prepared two training datasets. The first datasethas the sky as its background and is called the Sky Background Dataset (SBD).The second training dataset has more complex scenes (with diverse backgrounds)and is named the Complex Background Dataset (CBD). Additionally, two test setswere prepared: one containing clear images and the other with images with threerain artifacts, named the Rainy Test Set (RTS). This work also focuses onbenchmarking state-of-the-art object detection models, and to the best of ourknowledge, it is the first to investigate the performance of recent and popularvision-based object detection methods for UAV detection under challengingconditions such as complex backgrounds, varying UAV sizes, and low-to-heavyrainy conditions. The findings presented in the paper shall help provideinsights concerning the performance of the selected models for UAV detectionunder challenging conditions and pave the way to develop more robust UAVdetection methods. The codes and datasets are available at:https://github.com/AdnanMunir294/UAVD-CBRA.

Investigation of UAV Detection in Images with Complex Backgrounds and Rainy Artifacts