What is it about?
Recognizing threats in baggage X-ray scans is vital for safety in high-risk areas such as airports and shopping malls. With the uptick in terrorist activities over the past two decades, baggage threat identification has become paramount. Traditional methods are time-consuming and limited by human inspection capabilities. While deep learning frameworks have been introduced to enhance detection, they often grapple with class imbalance; prohibited objects are much less common than harmless ones in real-world baggage content. This research introduces a groundbreaking classification network utilizing the compound balanced affinity loss function, addressing this class imbalance. This function merges max-margin learning with effective sample representation. Our method, tested on COMPASS-XP and SIXray datasets, outperforms existing models, improving F1-score by 2.55% and 2.52% respectively, and achieving accuracies of 89.16% and 70.31%. To our knowledge, this is the pioneering contour-driven framework employing a compound loss function for imbalanced threat classification.
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Why is it important?
In the realm of X-ray threat identification, conventional methods often falter due to their reliance on human interpretation and the inherent class imbalance of datasets. Addressing these challenges, our research introduces a groundbreaking loss function: the compound balanced affinity loss. This innovation hinges on two pivotal insights: a) the pronounced difficulty in distinguishing threat from benign baggage content using traditional algorithms, and b) the need for a nuanced approach to tackle dataset imbalances. Our novel loss function seamlessly integrates max-margin learning with adept sample representation, ensuring a more precise and efficient threat detection.
Perspectives
Drafting this research paper was both challenging and enlightening. Working alongside esteemed colleagues, many of whom I've had the privilege of collaborating with previously, enriched the entire process. Delving deep into the intricacies of threat detection through X-ray scans unveiled several layers of complexities that often go unnoticed in day-to-day security protocols. But what truly invigorated me was our effort to address the class imbalance in detection methods – a problem that has been a bottleneck for many researchers in the field. Through this paper, I hope to not only shed light on an advanced detection mechanism but also underscore the importance of continuous innovation in ensuring safety. While the technicalities might seem overwhelming, the underlying message is clear: ensuring security is a shared responsibility, and every advancement, no matter how intricate, contributes to a safer world for all. Above all, I hope this research inspires further inquiry and sparks dialogue among peers, leading to even more refined solutions in the future.
Abdelfatah Ahmed
Khalifa University of Science Technology and Research
Read the Original
This page is a summary of: Highly Imbalanced Baggage Threat Classification, February 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3587716.3587736.
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