Put your computer vision models in “The Matrix” with synthetic data

As artificial intelligence advances on many fronts, computer vision continues to be one of the most, if not the most critical method for connecting the real and digital worlds. Computer vision is now well outside of niche implementations and use cases and is appealing to the mass market across all industries and applications. Despite its usefulness, computer vision is crippled by the nature of its real data which is messy, patchy and often very personal. Surprised? Do not be. Even with the overwhelming volume of image and video content created every day, most data may be unusable due to missing data, mislabeling and customer privacy concerns.

Input synthetic data for computer vision. Synthetic data itself is a broad category (which my colleague Jeremy Vale and I will describe and map in an upcoming report) and have a growing number of use cases across many industries. Computer vision is one of the most advanced application areas for synthetic data, and the use cases are growing. Think your business has no room for synthetic data? Well, if there’s a place where your business process interacts with real people or assets, maybe it’s time to reconsider.

Synthetic data emphasizes computer vision

There are a huge number of publicly available image and video datasets to train machine learning models, so what is the appeal of synthetic data? For companies that are working on more specialized use cases, that have complex and evolving data labeling requirements, or even that are trying to innovate in entirely new lines of business, these datasets will be probably very incomplete and ineffective. Instead, companies are leveraging tools that allow them to programmatically generate and customize image and video data that meets the needs of the challenge they’re trying to solve. Some of these use cases include:

  • Preventive maintenance. Your company must predict when a train coupling will eventually fail, and the only method is visual inspection. How will a computer vision model know when this coupling is in danger of losing its integrity? This can happen when a model is trained on a synthetic dataset that was generated to show a wide variety of different scenarios for a failing widget. The synthetic data set can be generated using any of the many tools available and verified by the technical knowledge of the employees.
  • Driver safety. Self-driving cars have been an important application of synthetic data over the past decade. Even as it becomes clear that most of us will need to keep our hands on the wheel, synthetic data offers a wealth of additional applications in and around vehicles. For example, in-vehicle driver monitoring is becoming a consumer and regulatory requirement in many markets. Generating real data for this can be extremely expensive, error prone, and the results are not adjustable or flexible. Synthetic data tools allow companies to define their needs and take into account all known usage scenarios.
  • Active customer engagement. Businesses want to better interact with their customers and build relationships that often require an understanding of their reactions and emotions. Training models capable of understanding and making decisions based on human facial data have important and obvious privacy and security implications, especially in markets where governments have begun to step in to regulate digital privacy ( for example, the EU with its GDPR).

Build a multiverse to train models with synthetic data

Turns out, creating a synthetic universe doesn’t have to be that hard. One of the most accessible techniques for companies getting into creating synthetic data for computer vision is to use popular commercial game engines like Unity or Unreal. These platforms enable the rapid generation of highly customizable landscapes and interactions as well as high graphical fidelity. Essentially, for building computer vision models, they also provide easy and flexible paths for labeling and tagging data for training. For companies getting into more complex and niche use cases (e.g. requiring thermal or x-ray data), there is a burgeoning landscape of vendors offering their own offerings built with specialized engines ( such as Sky Engine AI or Datagen). There is an opportunity in almost every industry today to leverage the growing capabilities of computer vision to optimize business models and gain competitive advantage, and synthetic data offers a way to open the eyes of computer vision. computer for your business.

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About Florence L. Silvia

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