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

The deep-learning-based tunnel accident detection (TAD) system (Lee 2019) hasinstalled a system capable of monitoring 9 CCTVs at XX site in November, 2018.The initial deep-learning training was started by studying 70,914 labeledimages and label data. However, sunlight, the tail light of a vehicle, and thewarning light of the working vehicle were recognized as a fire, and manypedestrians were detected in the lane of the tunnel or a black elongated blackobject. To solve these problems, as shown in Fig. 1, the false detection datadetected in the field were trained with labeled data and reapplied in thefield. As a result, false detection of pedestrians and fire could besignificantly reduced.

Self-enhancement of automatic tunnel accident detection (TAD) on CCTV by AI deep-learning