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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Semantic segmentation methods are used in au-tonomous car development to label pixels of road images (e.g. street, building, pedestrian, car, and so on). DeepLabv3+ and PSPNet are two of the best performance semantic segmentation methods according to Cityscapes benchmark. Although these methods achieved a very high performance with clear road images, yet these two methods are not tested under severe imaging conditions. In this work, we provided new Cityscapes datasets with severe imaging conditions: foggy, rainy, blurred, and noisy datasets. We evaluated the performance of DeepLabv3+ and PSPNet using our datasets. Our work demonstrated that although these models have high performance with clear images, they show very weak performance among the different imaging challenges. We proved that the road semantic segmentation methods must be evaluated using different kinds of severe imaging conditions to ensure the robustness of these methods in autonomous driving.

How Images Defects in Street Scenes Affect the Performance of Semantic Segmentation Algorithms