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

In the last two decades, baggage scanning has globally become one of theprime aviation security concerns. Manual screening of the baggage items istedious, error-prone, and compromise privacy. Hence, many researchers havedeveloped X-ray imagery-based autonomous systems to address these shortcomings.This paper presents a cascaded structure tensor framework that canautomatically extract and recognize suspicious items in heavily occluded andcluttered baggage. The proposed framework is unique, as it intelligentlyextracts each object by iteratively picking contour-based transitionalinformation from different orientations and uses only a single feed-forwardconvolutional neural network for the recognition. The proposed framework hasbeen rigorously evaluated using a total of 1,067,381 X-ray scans from publiclyavailable GDXray and SIXray datasets where it outperformed the state-of-the-artsolutions by achieving the mean average precision score of 0.9343 on GDXray and0.9595 on SIXray for recognizing the highly cluttered and overlappingsuspicious items. Furthermore, the proposed framework computationally achieves4.76\% superior run-time performance as compared to the existing solutionsbased on publicly available object detectors

Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from X-ray Scans