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Put Your Computer Vision Models In “The Matrix” With Synthetic Data


While artificial intelligence is charging forward in many fields, computer vision continues to be one of the most, if not the most. the mostly, critical approach to connecting the real and digital worlds. Computer vision is no longer in the implementation niches and use cases and has mass-market appeal across industries and applications. Despite its usefulness, computer vision is damaged by the nature of real-world data being chaotic, cluttered, and often too personal. Surprised? Don’t want to. Even with much of the image and video content created on a daily basis, most of the data may become unusable due to lost data, mis -marking, and concerns for customer privacy.

Entering synthetic data for computer vision. Synthetic data itself is a broad category (which my colleague Jeremy Vale and I will describe and map in a future report) and there are a growing number of use cases in many industries. Computer vision is one of the most advanced application features for synthetic data, and there is an ever-expanding number of use cases. Think your business has no room for synthetic data? Well, if there is any area where your business process interacts with real people or assets, it may be time to rethink.

Synthetic Data Sharpens Focus On Computer Vision

There are a significant number of image and video data sets available to the public to train machine learning models, so what is the appeal of synthetic data? For businesses that work with multiple niche use cases, have complex and changing data labeling requirements, or are even trying to adapt to entirely new lines of business, these data sets are likely to be incomplete and ineffective. Instead, companies use tools that allow them to create and adapt image and video data that meet the needs of the challenge they are trying to meet. Some of these use cases include:

  • Preventive maintenance. Your company needs to predict when a train connection will fail, and the only way is visual inspection. How can a computer vision model know when the integrity of that connection will be lost? This can happen when a model is trained on a synthetic set of data designed to display several different scenarios for a failed widget. The synthetic data set can be created using one of the many tools available and validated by the employee’s technical knowledge.
  • Driver safety. Self -driving cars have been an important application for synthetic data over the past decade. Although it has become clear that most of us need to keep our hands on the wheel, synthetic data offers many more applications in and around cars. For example, onboard driver monitoring has become a consumer and regulatory requirement in many markets. Creating real data for this can be very expensive, prone to errors, and the results are not adjustable and flexible. Synthetic data tools allow companies to define their needs and consider all known user scenarios.
  • Active customer engagement. Businesses want to better interact with their customers and build relationships that always require understanding their reactions and emotions. Training models to understand and make decisions based on human face data have significant and obvious implications for privacy and security, especially in markets where governments are already beginning to regulate digital privacy (e.g. , the EU with its GDPR).

Creating a Multiverse To Train Models with Synthetic Data

It turns out that creating a synthetic universe doesn’t have to be that difficult. One of the quickest ways for businesses to start creating synthetic data for computer vision is to use popular commercial gaming machines such as Unity or Unreal. These platforms allow for quick creation of more customized scenes and interactions as well as high graphic fidelity. Critically, for building computer vision models, they also offer quick and flexible routes to label and tag data for training. For businesses going into more complex and niche use cases (e.g., requiring thermal or X-ray data), there is an evolving view of vendors offering their own offerings. built with special engines (such as Sky Engine AI or Datagen). There is an opportunity now in almost every industry to take advantage of the expanding capabilities of computer vision to optimize business models and gain a competitive advantage, and synthetic data offers a way to open the eyes of computer vision for in your business.

Have a lot of questions? Please schedule a call with me via Forrester question.



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