
Overview
NavInfo Europe's face and license plate anonymizer detects and blurs recognisable faces and license plates in images. The blurring of faces and license plates helps to reach global privacy standards. The models are trained using images taken from a dashcam. For any specific solution, contact us to create a model to solve your needs
The average precision, average recall and F1-score for License Plates are 98.5, 99.42 and 98.96 respectively when testing on public datasets (CCPD). For faces these are 95.59, 98.05 and 96.80 respectively (IJB-C). Training set that was used was created by Navinfo Europe.
Highlights
- NavInfo Europe Face and License Plate Anonymizer can use a number of different models for detecting license plates and faces. All models that were used are available under MIT license or Apache License. The models were trained using a dedicated training set that was developed by Navinfo Europe. Images in this dataset were created by Navinfo Europe or used under license. Latency metrics: measured on g4dn.xl : 41fps measured on g4dn.12xl : 77fps
- The models and the training data for this pipeline can be used commercially.
- Navinfo Europe has created these models using more than 5 years of experience in the area of machine learning and AI.
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.p3.2xlarge Inference (Batch) Recommended | Model inference on the ml.p3.2xlarge instance type, batch mode | $0.50 |
ml.g4dn.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g4dn.xlarge instance type, real-time mode | $0.50 |
ml.p2.xlarge Inference (Batch) | Model inference on the ml.p2.xlarge instance type, batch mode | $0.50 |
ml.p3.8xlarge Inference (Batch) | Model inference on the ml.p3.8xlarge instance type, batch mode | $0.50 |
ml.p2.8xlarge Inference (Batch) | Model inference on the ml.p2.8xlarge instance type, batch mode | $0.50 |
ml.p2.16xlarge Inference (Batch) | Model inference on the ml.p2.16xlarge instance type, batch mode | $0.50 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $0.50 |
ml.p2.xlarge Inference (Real-Time) | Model inference on the ml.p2.xlarge instance type, real-time mode | $0.50 |
ml.g4dn.4xlarge Inference (Real-Time) | Model inference on the ml.g4dn.4xlarge instance type, real-time mode | $0.50 |
ml.m5.4xlarge Inference (Real-Time) | Model inference on the ml.m5.4xlarge instance type, real-time mode | $0.50 |
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Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Version release notes
This solution is based on the Nanodet and HardNet68 models which have been improved by Navinfo Europe B.V. Other variations also exist (Yolo, SSD, …) and are available on request.
Additional details
Inputs
- Summary
Upload the images you want to anonymize by blurring, we support the most common formats.
- Limitations for input type
- Each image should not exceed 5 MB. Maximum image resolution: 4096 x 2048.
- Input MIME type
- image/jpeg, image/png, image/x-ms-bmp, image/x-portable-pixmap, image/x-portable-anymap, image/tiff, image/jp2
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Navinfo Europe provides support upon request by the customer. Please contact our support engineers or sales representatives using the following e-mail address: awssupport@navinfo.euÂ
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