Threat Detection using Machine Learning in Public Places

Abstract

Airports are subject to tremendous demand and at- tempt to meet a variety of interconnected performance objectives. Airport aspects, such as safety and security, also include inside baggage detection of dangerous items. This subject is irrespective of the size and importance of the airport. A large proportion of aviation safety is the identification of risk objects with X-ray baggage scanning pictures. Currently, most screens still depend largely on human specialists to discover potentially dangerous items manually. Presently, automated learning, which enables computers to find solutions to problems themselves, is the most fascinating branch of artificial intelligence. The study introduces a threat object identification system in ML, such as Open-CV and Keras with Tensor Flow using the convolutions of neural neural networks and specific libraries. The themes discussed include the creation of the neural network, data increase strategies utilised, testing and detection rates utilising X-ray images.

Status - Published

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