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

Robust semantic scene segmentation for automotive applications is achallenging problem in two key aspects: (1) labelling every individual scenepixel and (2) performing this task under unstable weather and illuminationchanges (e.g., foggy weather), which results in poor outdoor scene visibility.Such visibility limitations lead to non-optimal performance of generalised deepconvolutional neural network-based semantic scene segmentation. In this paper,we propose an efficient end-to-end automotive semantic scene understandingapproach that is robust to foggy weather conditions. As an end-to-end pipeline,our proposed approach provides: (1) the transformation of imagery from foggy toclear weather conditions using a domain transfer approach (correcting for poorvisibility) and (2) semantically segmenting the scene using a competitiveencoder-decoder architecture with low computational complexity (enablingreal-time performance). Our approach incorporates RGB colour, depth andluminance images via distinct encoders with dense connectivity and featuresfusion to effectively exploit information from different inputs, whichcontributes to an optimal feature representation within the overall model.Using this architectural formulation with dense skip connections, our modelachieves comparable performance to contemporary approaches at a fraction of theoverall model complexity.

Multi-Model Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation