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

We propose a novel method utilizing an objectness score for maintaining thelocations and classes of objects detected from Mask R-CNN during mobile robotnavigation. The objectness score is defined to measure how well the detectoridentifies the locations and classes of objects during navigation.Specifically, it is designed to increase when there is sufficient distancebetween a detected object and the camera. During the navigation process, wetransform the locations of objects in 3D world coordinates into 2D imagecoordinates through an affine projection and decide whether to retain theclasses of detected objects using the objectness score. We conductedexperiments to determine how well the locations and classes of detected objectsare maintained at various angles and positions. Experimental results showedthat our approach is efficient and robust, regardless of changing angles anddistances.

An Objectness Score for Accurate and Fast Detection during Navigation