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Automatic Baggage Threat Detection Using Deep Attention Networks

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Artificial Intelligence Research (SACAIR 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1551))

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Abstract

Detecting threats in densely packed luggage is challenging for aviation security due to the partial occlusion or self-occlusion of prohibited items. Computer-aided systems have assisted security personnel to an extent. However, they require in the loop manipulation of X-ray baggage images to improve visibility of concealed prohibited items. Researchers have proposed several methods to detect threats automatically, but the occlusion problem is still prevalent. This paper proposes a novel attention mechanism that leverages spatial and channel-wise information of a given intermediate feature map. The mechanism can be seamlessly placed into existing Deep Convolutional Neural Network (DCNN) architectures. It sequentially infers the channel and spatial attention that recalibrates feature responses of the network by highlighting visual cues and dulling cues that do not contribute to the semantics of an image. In our experimentation, the proposed attention mechanism is implemented into Faster Region-based Convolutional Neural Network (Faster-RCNN) and thoroughly validated on publicly available datasets such as OPXray, SIXray and HIXray. It outperforms prior methods on the OPIXray, achieving a mean average precision (mAP) of 91.20%. For completeness, we also validate the proposed approach on ImageNet and MS-COCO datasets; it achieves an accuracy of 77.12 top-1 and 93.46 top-5 on ImageNet; and 39.7 mAP on MS-COCO.

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Acknowledgement

This research is funded and supported by the National Research Foundation (NRF), University of KwaZulu-Natal (UKZN) and the Center for High Performance Computing (CHPC).

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Authors and Affiliations

  1. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa

    Yashay Rampershad, Serestina Viriri & Mandlenkosi Gwetu

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  1. Yashay Rampershad
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  2. Serestina Viriri
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  3. Mandlenkosi Gwetu
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Corresponding author

Correspondence to Serestina Viriri .

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Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Edgar Jembere

  2. University of Pretoria, Pretoria, South Africa

    Aurona J. Gerber

  3. University of KwaZulu-Natal, Durban, South Africa

    Serestina Viriri

  4. University of KwaZulu-Natal, Durban, South Africa

    Anban Pillay

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Rampershad, Y., Viriri, S., Gwetu, M. (2022). Automatic Baggage Threat Detection Using Deep Attention Networks. In: Jembere, E., Gerber, A.J., Viriri, S., Pillay, A. (eds) Artificial Intelligence Research. SACAIR 2021. Communications in Computer and Information Science, vol 1551. Springer, Cham. https://doi.org/10.1007/978-3-030-95070-5_11

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  • DOI: https://doi.org/10.1007/978-3-030-95070-5_11

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