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

Sensors

Ernesto Damiani¹  Naoufel Werghi¹  Taimur Hassan¹  Muhammad Shafay¹  Mohammed Bennamoun²  Samet Akçay³  Salman Khan⁴ 

[1] Center for Cyber-Physical Systems, Khalifa University of Science and Technology, Abu Dhabi 127788, UAE;Department of Computer Science and Software Engineering, The University of Western Australia, Perth 6907, Australia;Department of Computer Science, Durham University, Durham DH1 3DE, UK;Inception Institute of Artificial Intelligence, Abu Dhabi 127788, UAE;

关键词: aviation security;    meta-transfer learning;    one-shot learning;    convolutional neural networks;    structure tensors;    X-ray imagery;   

DOI  :  10.3390/s20226450

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown