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

Lane detection plays a crucial role in autonomous driving by providing vitaldata to ensure safe navigation. Modern algorithms rely on anchor-baseddetectors, which are then followed by a label assignment process to categorizetraining detections as positive or negative instances based on learnedgeometric attributes. The current methods, however, have limitations and mightnot be optimal since they rely on predefined classical cost functions that arebased on a low-dimensional model. Our research introduces MatchNet, a deeplearning sub-module-based approach aimed at enhancing the label assignmentprocess. Integrated into a state-of-the-art lane detection network like theCross Layer Refinement Network for Lane Detection (CLRNet), MatchNet replacesthe conventional label assignment process with a sub-module network. Thisintegration results in significant improvements in scenarios involving curvedlanes, with remarkable improvement across all backbones of +2.8% for ResNet34,+2.3% for ResNet101, and +2.96% for DLA34. In addition, it maintains or evenimproves comparable results in other sections. Our method boosts the confidencelevel in lane detection, allowing an increase in the confidence threshold. Thecode will be available soon: https://github.com/sapirkontente/CLRmatchNet.git

CLRmatchNet: Enhancing Curved Lane Detection with Deep Matching Process