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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Concealed object detection in Terahertz imaging is an urgent need for publicsecurity and counter-terrorism. In this paper, we provide a public dataset forevaluating multi-object detection algorithms in active Terahertz imagingresolution 5 mm by 5 mm. To the best of our knowledge, this is the first publicTerahertz imaging dataset prepared to evaluate object detection algorithms.Object detection on this dataset is much more difficult than on those standardpublic object detection datasets due to its inferior imaging quality. Facingthe problem of imbalanced samples in object detection and hard trainingsamples, we evaluate four popular detectors: YOLOv3, YOLOv4, FRCN-OHEM, andRetinaNet on this dataset. Experimental results indicate that the RetinaNetachieves the highest mAP. In addition, we demonstrate that hiding objects indifferent parts of the human body affect detection accuracy. The dataset isavailable at https://github.com/LingLIx/THz_Dataset.

Active Terahertz Imaging Dataset for Concealed Object Detection