Abstract
Screening baggage against potential threats has become one of the prime aviation security concerns all over the world, where manual detection of prohibited items is a time-consuming and hectic process. Many researchers have developed autonomous systems to recognize baggage threats using security X-ray scans. However, all of these frameworks are vulnerable against screening cluttered and concealed contraband items. Furthermore, to the best of our knowledge, no framework possesses the capacity to recognize baggage threats across multiple scanner specifications without an explicit retraining process. To overcome this, we present a novel meta-transfer learning-driven tensor-shot detector that decomposes the candidate scan into dual-energy tensors and employs a meta-one-shot classification backbone to recognize and localize the cluttered baggage threats. In addition, the proposed detection framework can be well-generalized to multiple scanner specifications due to its capacity to generate object proposals from the unified tensor maps rather than diversified raw scans. We have rigorously evaluated the proposed tensor-shot detector on the publicly available SIXray and GDXray datasets (containing a cumulative of 1,067,381 grayscale and colored baggage X-ray scans). On the SIXray dataset, the proposed framework achieved a mean average precision (mAP) of 0.6457, and on the GDXray dataset, it achieved the precision and F1 score of 0.9441 and 0.9598, respectively. Furthermore, it outperforms state-of-the-art frameworks by 8.03% in terms of mAP, 1.49% in terms of precision, and 0.573% in terms of F1 on the SIXray and GDXray dataset, respectively.
Highlights
Baggage threat recognition has gained the utmost attention due to increased terrorist activities, especially in the last two decades
Apart from this, we have proposed a novel detection strategy, dubbed Cascaded Structure Tensor (CST), to recognize cluttered, occluded, and overlapping items from the Security Inspection X-ray (SIXray) [14] and GDXray [15] datasets
Apart from quantitative evaluations, we demonstrate the capacity of the proposed framework for accurately detecting the cluttered, concealed, and overlapping contraband items through extensive qualitative examples
Summary
Baggage threat recognition has gained the utmost attention due to increased terrorist activities, especially in the last two decades. To identify baggage threats at the airport, malls, and cargoes, radiography is mainly used due to its reliability and cost-effectiveness [2]. Many researchers have quantitatively measured the detection capacity of the security officers towards recognizing baggage threats through X-ray imagery via receiver operator characteristics (ROC) curve [3]. Sensors 2020, 20, 6450; doi:10.3390/s20226450 www.mdpi.com/journal/sensors (within the X-ray scans) to identify potential threats is a time-consuming task [4]. Researchers have reported the high capacity (and less false alarm rate) of sniffer dogs to detect suspicious items as compared to humans. Sniffer dogs can only work for an hour or so before they need rest [6]
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