Sold by: MphasisÂ
Deployed on AWS
This solution analyzes images of civil structure surfaces and predicts whether they have cracks or not, using quantum ML.
Overview
This is a hybrid classical-quantum machine learning based solution which detects cracks on concrete and other civil infrastructure surface images. The algorithm uses a pre-trained DCGAN before variational quantum circuit. The DCGAN model is trained on in hand dataset to perform feature transformation. This trained DCGAN model enhances feature to be used as input in quantum architecture. The algorithm used in this solution inherits variational quantum circuit layers with trained parameters dedicated for surface crack image classification.
Highlights
- The appearance of cracks and distortions can be visually unattractive and disconcerting for occupants, and if left untreated they can affect the integrity, safety and stability of the structure. In case of railway bridge, flyover or foot bridge, it is crucial to regularly inspect the structures for cracks or any other defects. this solution can be used by agencies like Municipalities, review boards, construction comapanies to moniter the civil structure health and take corrective action when necessary.
- Quantum based Surface Crack Detection solution analyzes the images of concrete 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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Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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. Bug Fixes.
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
https://github.com/Mphasis-ML-Marketplace/QML-based-Infrastructure-Crack-Detection/blob/main/input.zip
https://github.com/Mphasis-ML-Marketplace/QML-based-Infrastructure-Crack-Detection/blob/main/data/input/batch/input.zip
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