Sold by: Mphasis
Deployed on AWS
This solution analyzes images of road surfaces and predicts whether they have damages like cracks, potholes, etc. or not.
Overview
This is a hybrid classical-quantum machine learning based solution which detects cracks on road surface images. The algorithm uses integrated trainable classical convolutional encoder before variational quantum circuit. This jointly trained convolutional encoder-based architecture enables dimensionality reduction and reduces non-linearity which helps in simplified representation of data to be sent as an input to variational quantum circuit. The algorithms used in this solution inherits variational quantum circuit layers with trained parameters dedicated for road damage image classification.
Highlights
- Surface damages like cracks, potholes, etc are the major reasons in degradation of the roads, which eventually leads to significant damage to and unusability of roads. Maintaining a vaible road infrastructure fit to serve commercial and personal vehiculer traffic along with human safety, requires regular inspection of road quality and therefore maintenance. This can be used by Municipalities and road construction agencies to monitor the road condition and take corrective action when required.
- Quantum based Road Damage Detection solution analyzes the images of road surfaces and predicts presence or absence of cracks. The current solution provides quantum ML based alternative to state of the art classifical deep learning based image classification systems.
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Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $40.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $20.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $40.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $40.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $40.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $40.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $40.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $40.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $40.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $40.00 |
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Delivery details
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.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
This is version 2.1.
Additional details
Inputs
- Summary
- The input dataset should be a zip folder containing images in png format.
- Input zip folder should not contain more than 5 images.
- Input MIME type
- application/zip, text/csv, text/plain
https://github.com/Mphasis-ML-Marketplace/Quantum-ML-Based-Road-Damage-Detection/tree/main/input
https://github.com/Mphasis-ML-Marketplace/Quantum-ML-Based-Road-Damage-Detection/tree/main/input
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Vendor resources
Support
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