April 18, 2025
by Ellie Gabel

Machine vision systems are becoming increasingly common across multiple industries. Manufacturers use them to streamline quality control, self-driving vehicles implement them to navigate, and robots rely on them to work safely alongside humans. Amid these rising use cases, design engineers must focus on the importance of reliable and cost-effective optical technologies.

While artificial intelligence (AI) algorithms may take most of the spotlight in machine vision, optical systems providing the data these models analyze are crucial, too. Therefore, by designing better camera and sensor arrays, design engineers can foster optimal machine vision on several fronts.

Optical systems are central to machine vision accuracy before the underlying AI model starts working. These algorithms are only effective when they have sufficient relevant data for training, and this data requires cameras to capture it.

Some organizations have turned to using AI-generated synthetic data in training, but this is not a perfect solution. These images may contain errors and hallucinations, hindering the model’s accuracy. Consequently, they often require real-world information to complement them, which must come from high-quality sources.

Developing high-resolution camera technologies with large dynamic ranges gives AI teams the tools necessary to capture detailed images of real-world objects. As a result, it becomes easier to train more reliable machine vision models.

Expanding machine vision applications

Machine vision algorithms need high-definition visual inputs during deployment. Even the most accurate model can produce inconsistent results if the images it analyzes aren’t clear or consistent enough.

External factors like lighting can limit measurement accuracy, so designers must pay attention to these considerations in their optical systems, not just the cameras themselves. Sufficient light from the right angles to minimize shadows and sensors to adjust the focus accordingly can impact reliability.

Next, video data and still images are not the only optical inputs to consider in a machine vision system. Design engineers can also explore a variety of technologies to complement conventional visual data.

For instance, lidar is an increasingly popular choice. More than half of all new cars today come with at least one radar sensor to enable functions like lane departure warnings. So, lidar is following a similar trajectory as self-driving features grow.

Complementing a camera with lidar sensors can provide these machine vision systems with a broader range of data. More input diversity makes errors less likely, especially when operating conditions may vary. Laser measurements and infrared cameras could likewise expand the roles machine vision serves.

The demand for high-quality inputs means the optical technologies in a machine vision system are often some of its most expensive components. By focusing on developing lower-cost solutions that maintain acceptable quality levels, designers can make them more accessible.

It’s worth noting that advances in camera technology have already brought the cost of such a solution from $1 million to $100,000 on the high end. Further innovation could have a similar effect.

Machine vision needs reliable optical technologies

AI is only as accurate as its input data. So, machine vision needs advanced optical technologies to reach its full potential. Design engineers hoping to capitalize on this field should focus on optical components to push the industry forward.

Ellie Gabel is a freelance writer as well as an associate editor at Revolutionized.