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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Object detection from Unmanned Aerial Vehicles (UAVs) is of great importancein many aerial vision-based applications. Despite the great success of genericobject detection methods, a significant performance drop is observed whenapplied to images captured by UAVs. This is due to large variations in imagingconditions, such as varying altitudes, dynamically changing viewing angles, anddifferent capture times. These variations lead to domain imbalances and, thus,trained models suffering from domain bias. We demonstrate that domain knowledgeis a valuable source of information and thus propose domain-aware objectdetectors by using freely accessible sensor data. By splitting the model intocross-domain and domain-specific parts, substantial performance improvementsare achieved on multiple data sets across various models and metrics withoutchanging the architecture. In particular, we achieve a new state-of-the-artperformance on UAVDT for embedded real-time detectors. Furthermore, we create anew airborne image data set by annotating 13,713 objects in 2,900 imagesfeaturing precise altitude and viewing angle annotations.

Diminishing Domain Bias by Leveraging Domain Labels in Object Detection on UAVs