Heavenly insight and the need for accurately detecting faults in a manufacturing setting.
What it does
Magic, but maybe also automatically process files uploaded via Azure IoT Hub, detect and log any defects and mark them in the image with a bounding box.
How we built it
- Azure custom vision to train and detect faults
- Azure functions to trigger the pipeline once a new image is uploaded
- Azure Cosmos DB and blob storage for storing files and data
- Power BI to display relevant info to the appropriate people
Challenges we ran into
Deciding on the proper data augmentation technique and correctly hooking up the cosmos db to PowerBI
Accomplishments that we're proud of
The high accuracy of our model and great UI/UX
What we learned
The meaning of life itself and how to properly use the Azure product suite.
What's next for DeepDefectDetector
World domination. Further integration into a real-world process.
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