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

Deep learning has been widely recognized as a promising approach in differentcomputer vision applications. Specifically, one-stage object detector andtwo-stage object detector are regarded as the most important two groups ofConvolutional Neural Network based object detection methods. One-stage objectdetector could usually outperform two-stage object detector in speed; However,it normally trails in detection accuracy, compared with two-stage objectdetectors. In this study, focal loss based RetinaNet, which works as one-stageobject detector, is utilized to be able to well match the speed of regularone-stage detectors and also defeat two-stage detectors in accuracy, forvehicle detection. State-of-the-art performance result has been showed on theDETRAC vehicle dataset.

Focal Loss Dense Detector for Vehicle Surveillance