Integration of AI & Baggage Scanners

Published 17 Dec, 2020

Let us start with the rudimentary know-what about baggage scanners. Baggage/Luggage scanners are X-Ray machines, in which, as our luggage passes through the lead-lined curtains, an x-ray image of the inside content is created. We can usually find such scanners stationed at locations such as airports and railway stations. The images formed are colour divided, depending upon the density of the objects.

WHY ARE THEY IMPORTANT?

Millions of people travel every day. Security checkpoints are very crucial to maintain at airports and stations. To keep the passengers safe and secure, it is vital to check what is inside their baggage and make sure that they are following the security norms and comply with the restrictions laid down by the authorities.

HOW IS AI HELPING?

Baggage handling and security is a colossal and complicated task for the airport authorities. With a large number of people entering and exiting every minute, long lines, delays and pushing carry-ons into the scanner can create chaos. Manual detection of banned items in the x-ray images can add on to the problem as it slows down the process. They may also miss subtle new differences due to how the newly introduced consumer devices are wired or put together.

A suggestive measure can be to bring machine learning and artificial intelligence into security and improve the accuracy of the scanners.

Data architects and software engineers are working on developing solutions that can easily be fused into the existing algorithms. This procedure will help automate detection training in scanners, enabling the ‘intuitive identification’ feature.

By using such a formula, the airport authorities could retreat from using expensive proprietary detection capabilities from their hardware. While doing so, they can also avoid labor intensive hand searches.