OneBoxVision Blog

Releasing Machine Vision from it's chains

MONEY IS POURING into the Internet of Things, built around smart sensors connected to the Internet. But the richest sense of all, vision, has largely been bypassed, used in industry for only a fraction of its potential. Machine vision has been isolated in a technological backwater.

Until recently, computer vision — used most widely in manufacturing — and mainstream computing technology have existed in parallel worlds. Along with other factory floor technologies, computer vision tends to be machine-specific, hardware driven, and makes little if any use of the Internet. Many the advances we take for granted in modern computing — ubiquitous connectivity, unlimited data storage in the cloud, insights drawn from massive unstructured data sets — have yet to be applied systematically to the factory floor in general and to computer vision specifically.


vision.jpg

It’s no surprise when you consider that until recently most computer vision software was written by computer vision hardware makers, built on embedded systems without open APIs. What comes to mind when you think of the software that came bundled with your scanner, your Wi-Fi router, your car’s navigation system? Balky, inflexible and unintuitive. The software isn’t much more than a utility to run the hardware.

But this closed world is being broken open by a convergence of emerging technologies:

  • The proliferation of cheap, high pixel-density camera sensors
  • Open implementations of vision algorithms, machine learning, and statistical tools
  • Large amounts of cheap computing power, becoming virtually limitless in the cloud

These technologies offer all the raw materials needed for a massive shift in how computer vision is practiced. It’s a shift from focusing on the raw material of visual data — the pixels and bitmaps generated by specific cameras — to extracting data from images and using statistical and data science techniques to draw insights.

This new approach to computer vision has a powerful application amid a manufacturing renaissance emphasizing rapid product cycles and mass customization. Whereas the archetypal factory was built around systematic, repeatable function, modern manufacturing is about flexibility, adaptability and high efficiency. 

Quality Demands always increase

But that need for flexibility on the manufacturing line is in tension with unrelenting quality demands that manufacturers face across industries and down supply chains. Despite huge investments in quality control, automakers recalled nearly as many cars as they sold in the U.S. in 2012. Ford and GM made warranty payments of $5.7 billion in 2012, more than half of the $10.5 billion they reported in net income. Automakers are now paying suppliers prices based on benchmarks like defects per million, terminating those who fall below thresholds, and pushing liability for warranty claims down to their suppliers.

While automation has transformed much of manufacturing, a surprising amount of quality control is still done by hand or otherwise relies on human judgement. Many types of inspection require visual evaluation, but manfacturers’ experience with computer vision in quality control has been a frustrating one. Walk into a factory and ask the manager about computer vision, and you are likely to hear a variant of, “Oh yeah, we tried that, it didn’t work very well, we had to throw it out.”

Existing machine vision uses a 30-year-old architecture that’s capital-intensive and severely constrained in its abilities. Today’s computer vision systems operate as stand-alone islands, rarely connected to the Internet. Every time needs change, each installation has to be manually reprogrammed, unit by unit.

Worse still, little data is kept, making it difficult to spot trends or find correlations among multiple variables. Most manufacturing quality inspection by machine vision today is pass/fail. If the initial inspections of a production run pass the quality inspection, the machines are turned on and the testing data overwritten.

Vision used in analytics

The new computer vision, liberated from its hardware shackles and empowered by connectivity, unlimited data storage and Big Data-style statistical analysis, is beginning to change the role of vision in manufacturing. Instead of being a reactive tool to detect defects, computer vision is becoming a data collection tool supporting defect prevention initiatives, improving understanding of complex processes, and enabling greater collaboration across entire supply chains in real time.

With modern web services, once the data is collected it is easily aggregated into dashboards and distributed to production workers, quality engineers, and management, locally or around the globe. Manufacturers can share data with supply chain partners, making it easier to monitor their suppliers or to satisfy reporting requirements for customers.

Once the captured data is in the cloud, such systems can store an unlimited amount of data indefinitely, for reanalysis and retrieval anytime. They let plants run correlations over time, track trends and identify root causes, and as new variables of interest arise, go back and analyze previously acquired data.

As each plant gets smarter, the whole system gets smarter. Like Google learning more about consumers with their every search and click, we’re able to aggregate our learnings from quality issues common across industries.

Vision networks are the answer

Ultimately, vision can turn physical world challenges into Big Data problems. We know how to solve these Big Data problems better and better every day. OneBoxVision is an expert in building inspection systems for industrial manufacturing. Download our whitepaper on building vision networks and take the first step to liberating your vision data. 

DOWNLOAD PAPER ON VISION NETWORKS

 

Topics: Building vision networks IOT Industry 4.0