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In the last two decades, luggage scanning has globally become one of theprime aviation security concerns. Manual screening of the baggage items is acumbersome, subjective and inefficient process. Hence, many researchers havedeveloped Xray imagery-based autonomous systems to address these shortcomings.However, to the best of our knowledge, there is no framework, up to now, thatcan recognize heavily occluded and cluttered baggage items from multi-vendorX-ray scans. This paper presents a cascaded structure tensor framework whichcan automatically extract and recognize suspicious items irrespective of theirposition and orientation in the multi-vendor X-ray scans. The proposedframework is unique, as it intelligently extracts each object by iterativelypicking contour based transitional information from different orientations anduses only a single feedforward convolutional neural network for therecognition. The proposed framework has been rigorously tested on publiclyavailable GDXray and SIXray datasets containing a total of 1,067,381 X-rayscans where it significantly outperformed the state-of-the-art solutions byachieving the mean average precision score of 0.9343 and 0.9595 for extractingand recognizing suspicious items from GDXray and SIXray scans, respectively.Furthermore, the proposed framework has achieved 15.78% better time

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