Posted on 2021-08-18
Ryax Technologies Ryax Technologies

License plate detection

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Description

This model detects vehicle license plates images and transcribes them using the TensorFlow Object Detection API. It takes as input license plate images in a PNG or JPG format. The model produces json file as output.

Business benefit

This model can be used to identify vehicles for transportation planning and for traffic enforcement.

Relevant for

  • Detection specialists
  • Urban planners
  • Traffic surveillance
  • Transportation planning
  • Law enforcement
  • Other

Data inputs (mandatory)

▪ Images (1Mo max, .jpg .png)

Data Output

▪ Text file containing the detected license plate number (1Mo max, text file)

Technical description

PERFORMANCE METRICS
53.6% Precision and 97.5% Recall

The dataset used to train this model is not publicly available. This model uses precision and recall as its metrics. These are commonly used metrics in classification. This model achieves a recall of 97.52%, and precision of 53.64%. In practical terms this means that the model will most likely identify a license plate if it is present in an image but will also report about twice as many license plates as are truly present in a dataset.

A higher precision score indicates that the majority of labels predicted by the model for different classes are accurate.

A higher recall score indicates that the model finds and predicts correct labels for the majority of the classes it is supposed to find.

OVERVIEW
The only training information available about this model at its source repository is that the model was based upon the ResNet architecture.

TRAINING
Training dataset details are not publicly available.

VALIDATION
Validation dataset details are not publicly available.
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