X-ray security screening is widely utilized in aviation and transportation, and its importance has sparked interest in automated screening systems. The goal of this webinar is to explore computerized X-ray security imaging methods by classifying them into traditional machine learning and modern deep learning applications. The talk briefly reviews the traditional machine learning methodologies used in X-ray security imaging, and subsequently delves deeper into the applications of recent deep learning-based algorithms. The suggested taxonomy divides deep learning applications into supervised and unsupervised learning categories, with a focus on object categorization, detection, segmentation, and anomaly detection. The session goes on to look at some well-known X-ray datasets and presents a performance benchmark. The talk will be concluded with a discussion and future directions for X-ray security imagery, based on current and future advances in deep learning.
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