Dataset Information

Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats.

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.

SUBMITTER: Hassan T

PROVIDER: S-EPMC7696514 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Publications

Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats.

Hassan Taimur T Shafay Muhammad M Akçay Samet S Khan Salman S Bennamoun Mohammed M Damiani Ernesto E Werghi Naoufel N

Sensors (Basel, Switzerland) 20201112 22

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 ...[more]

PMID: 33198071

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Project description:BackgroundCriminal associates such as terrorist members are likely to deny knowing members of their network when questioned by police. Eye tracking research suggests that lies about familiar faces can be detected by distinct markers of recognition (e.g. fewer fixations and longer fixation durations) across multiple eye fixation parameters. However, the effect of explicit eye movement strategies to concealed recognition on such markers has not been examined. Our aim was to assess the impact of fixed-sequence eye movement strategies (across the forehead, ears, eyes, nose, mouth and chin) on markers of familiar face recognition. Participants were assigned to one of two groups: a standard guilty group who were simply instructed to conceal knowledge but with no specific instructions on how to do so; and a countermeasures group who were instructed to look at every familiar and unfamiliar face in the same way by executing a consistent sequence of fixations.ResultsIn the standard guilty group, lies about recognition of familiar faces showed longer average fixation durations, a lower proportion of fixations to the inner face regions, and proportionately more viewing of the eyes than honest responses to genuinely unknown faces. In the countermeasures condition, familiar face recognition was detected by longer fixations durations, fewer fixations to the inner regions of the face, and fewer interest areas of the face viewed. Longer fixation durations were a consistent marker of recognition across both conditions for most participants; differences were detectable from the first fixation.ConclusionThe results suggest that individuals can exert a degree of executive control over fixation patterns but that: the eyes are particularly attention-grabbing for familiar faces; the more viewers look around the face, the more they give themselves away; and attempts to deploy the same fixation patterns to familiar and unfamiliar faces were unsuccessful. The results suggest that the best strategy for concealing recognition might be to keep the eyes fixated in the centre of the screen but, even then, recognition is apparent in longer fixation durations. We discuss potential optimal conditions for detecting concealed knowledge of faces.

Dataset Information

Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats.

Publications

Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats.

Similar Datasets

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