Vehicle Classifier


The Vehicle Classifier is a deep learning-based algorithm designed to correctly interpret vehicle type including busses, pickups, sedans, bicycles, and more. Performing from both a car-mounted and elevated perspective, this algorithm is an asset for traffic data, security, and smart intersection applications, while conforming to a low power profile. It can be regularly updated with newly released vehicles according to your requirements.



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PERFORMANCE

Optimized for edge performance with a low power profile.

RANGE

Up to 300 meters.

CONDITIONS

Dawn to dusk.

ENVIRONMENT

Environmentally agnostic performance.

ARCHITECTURE

Backbone: Resnet-101; Detector: RefineDet.

FUNCTIONALITY

Detection.

INPUT SIZE

512x512.

SENSOR MODALITY

Visible light.



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Our services


  • MAINTENANCE

Each CVEDIA model comes backed with an ongoing maintenance agreement. Our team is available for continuous improvements and to ensure upkeep.


  • FINE GRAIN CLASSIFICATION

Speak to our team to discuss model classification as a service add on or as a separate model. CVEDIA’s in-house team will work with you to define your requirements, and build a synthetic-based model incorporating your classification specifications.


  • ADDITIONAL CLASSES

CVEDIA models are built using proprietary synthetic data technology, meaning adding additional classes to your model is possible in a matter of weeks.

Use cases


  • SMART CITIES

Use it as a reliable component of your smart intersection grid. The Vehicle Classifier performs with higher accuracy than traditional algorithms thanks to proprietary CVEDIA synthetic technology.


  • TRAFFIC AND DATA ANALYTICS

Calculate road usage analytics and highway traffic data with the ability to add new vehicle makes and models as they’re released.


  • SECURITY

Quickly localize vehicles of interest using security camera grids.


  • VEHICLE ADAS

Use it in your vehicle autonomy system to correctly classify vehicle size. This algorithm has been developed to maintain a low power profile, making it perfect for battery-powered vehicles and applications.