1. Meta-transfer learning driven tensor-shot detector for the autonomous localization and recognition of concealed baggage threats
- Author
-
Muhammad Shafay, Salman H. Khan, Mohammed Bennamoun, Taimur Hassan, Samet Akcay, Naoufel Werghi, and Ernesto Damiani
- Subjects
Computer science ,0211 other engineering and technologies ,structure tensors ,02 engineering and technology ,One-shot learning ,computer.software_genre ,lcsh:Chemical technology ,Biochemistry ,Convolutional neural network ,Article ,meta-transfer learning ,Analytical Chemistry ,aviation security ,convolutional neural networks ,0202 electrical engineering, electronic engineering, information engineering ,lcsh:TP1-1185 ,Electrical and Electronic Engineering ,Instrumentation ,one-shot learning ,021110 strategic, defence & security studies ,Detector ,Process (computing) ,Object (computer science) ,X-ray imagery ,Atomic and Molecular Physics, and Optics ,020201 artificial intelligence & image processing ,Data mining ,F1 score ,Transfer of learning ,computer - 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.
- Published
- 2020