Deep learning algorithm spots vehicular threats

| Environmental Testing

A first pass scan is all that’s needed to detect threats beneath the chassis of a vehicle

Self learning scanning device can spot the difference between a threat and a standard car componenet lying beneath the chassis.

An Ultraviolet threat detection system has been developed to instantly detect threats such as bombs, weapons, and drugs stowed in the undercarriage of vehicles using deep learning computer vision technology.

Developed by UVeye, UV Inspect employs an algorithm, which analyses each vehicle part separately to identify every element on the undercarriage of the vehicle and detect threats within seconds.

This advancement in detection capabilities is built on training the algorithms to truly understand what each part of a vehicle is supposed to look like, independent of make or model or the need for previous examples on record. Examples of the depth of this research become evident in the UV Inspect technology’s ability to identify items like an exhaust pipe without having previously scanned the vehicle.

According to Amir Hever of UVeye, as a first pass detection system, UV Inspect addresses the needs of border control operators and other security professionals, who need to examine non-repeat traffic to securely scan vehicles passing through their checkpoints.

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