Trainable Structure Tensors for Autonomous Baggage Threat Detection Under Extreme Occlusion
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Detecting baggage threats is one of the most difficult tasks, even for expert officers. Many researchers have developed computer-aided screening systems to recognize these threats from the baggage X-ray scans. However, all of these frameworks are limited in recognizing contraband items under extreme occlusion. This paper presents a novel instance detector that utilizes a trainable structure tensor scheme to highlight the contours of the occluded and cluttered contraband items (obtained from multiple predominant orientations) while simultaneously suppressing all the other baggage content within the scan, leading to robust detection. The proposed framework has been rigorously tested on four publicly available X-ray datasets where it outperforms the state-of-the-art frameworks in terms of mean average precision scores. Furthermore, to the best of our knowledge, it is the only framework that has been rigorously tested on combined grayscale and colored scans obtained from four different types of X-ray scanners.
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