  In the last two decades, baggage scanning has globally become one of the
prime aviation security concerns. Manual screening of the baggage items is
tedious, error-prone, and compromise privacy. Hence, many researchers have
developed X-ray imagery-based autonomous systems to address these shortcomings.
This paper presents a cascaded structure tensor framework that can
automatically extract and recognize suspicious items in heavily occluded and
cluttered baggage. The proposed framework is unique, as it intelligently
extracts each object by iteratively picking contour-based transitional
information from different orientations and uses only a single feed-forward
convolutional neural network for the recognition. The proposed framework has
been rigorously evaluated using a total of 1,067,381 X-ray scans from publicly
available GDXray and SIXray datasets where it outperformed the state-of-the-art
solutions by achieving the mean average precision score of 0.9343 on GDXray and
0.9595 on SIXray for recognizing the highly cluttered and overlapping
suspicious items. Furthermore, the proposed framework computationally achieves
4.76\% superior run-time performance as compared to the existing solutions
based on publicly available object detectors
