Abstract:
Baggage threats are a major concern worldwide, and in the US alone, around 1.5 million passengers are screened for dangerous items in their luggage. However, manually checking bags can be challenging, and errors can occur, making it crucial to have a way for machines to detect threats in bags without human intervention. The current methods rely on outdated detectors for regular X-ray images or require large amounts of data to train them for different scanners. However, detecting specific threats hidden inside bags is crucial for airport staff to determine what is safe and what is not. With advancements in technology, smarter methods are needed to deal with bag threats, as relying on people to check bags is time-consuming and can lead to overlooked threats. Our proposed idea is a specialized technique for identifying hidden threats in bags. It requires less training data than other methods and is less complex, making it easier for computers. Furthermore, unlike other sophisticated models, it does not require additional steps to determine the bag’s contents.
