Glass type detection using Watson visual recognition

In my previous article I designed and implemented a simple Proof of Concept to offers a pattern recognition solutions for automatic quality control in production processes. The IBM Watson feature extraction and machine learning techniques are used to design classification systems for a variety of image-based inspection tasks.

Watson visual recognition service is different from classic pattern matching visual recognition, because to recognize a glass it is not necessary to compare an glass image too similar to the matching model,  the service is based on a machine learning, in training phase the service learns to recognize a glass class by very different type of images for that glass class, so a glass can be recognize although the image is very different from matching model.

For the sparkling glass I trained the machine learning with the following images, as you know the service learned about what is a sparkling glass from very different type of sparkling glasses:

For the coupe glass I trained the machine learning with the following images:

and for the chianti glass I trained the machine learning with the following images:

As for the previous scenario I grouped the images to define a single classifier, I prepared a zip with positive examples images and a zip with negative examples images. I used a service rest API to create the classifier and complete the training phase.

I build my POC with node-RED  to automate the following end-to-end scenario:

• I simulated a visual inspection system for high-speed checking of wine glass production line
• The images are uploaded on a cloud storage
• A node-RED flow analyze periodically the uploaded images
• A quality score is calculated by Watson visual recognition API
• Maximo asset manager meters are updated with the piece produced, I created three meters associated to production line: coupe glass meter, sparkling glass meter and chianti glass meter
• A web dashboard shows in real-time the number of glasses produced for every class

Here is my node RED flow:

The node-RED flows is based on the following logic sections:

• nodes to fetch the image files from a cloud storage, I’m using google drive, where the image are uploaded from IoT devices attached to the production line

• nodes to assemble and call the rest-API’s URL of Watson visual recognition service

• nodes to send data to dtweet.io service, I’m using the freeboard web dashboard with a dtweet datasource

• nodes to assemble and call the rest-API’s URL of Maximo meters update service

Here is the demo dashboard, currently my POC is up&running, every 10 seconds a glass is produced:

Here is my Maximo application with the real-time meters:

thanks to reading !