Everything You Need to Know About Convolutional Neural Networks & Embedded Vision

A neural net

The advancement of machine learning and neural networks is happening at an astronomical rate, and more and more opportunities are being uncovered every day for their implementation. To help you understand how some of the more advanced data interpretation technologies work, we’re going over what embedded vision and convolutional neural networks are, as well as how they function together.

What is Embedded Vision?

Embedded vision refers to a monitoring technology - for example, a camera and a processing unit or computer - that is embedded within other technology. An excellent emerging example of this is a camera placed on the outside of a self-driving car. The camera records information in the form of pixels, which are then interpreted to some extent by the attached processing unit.

What is a Convolutional Neural Network (CNN)?

A convolutional neural network is an advanced technology used to interpret massive amounts of data with remarkable precision. It consists of multiple layers of interpretation. Typically, the first layer interprets patterns in a set of pixels, which would appear as a digital image to a human. The information is then passed to a second layer, and those patterns identified by the first are then interpreted. Patterns within the original patterns will help the neural network gather more information about the image. The information is continuously passed to additional layers for more patterns to be recognized until the neural network determines the original image's identity. The effect is a series of layers that can learn to interpret images.

How CNNs Work with Embedded Vision

While these two technologies are fascinating by themselves, combining them creates nearly unlimited machine learning capability. Embedded vision systems have high bandwidth and low power requirements, so they can feed large amounts of real-life data to a CNN for processing. The more data a CNN has to interpret and compare, the faster and more efficiently it learns and can avoid mistakes.

Combining the power of embedded vision with a convolutional neural network allows for real-time interpretation of large images or videos taken of the real world. Applications include traffic monitoring and optimization, automated parking spot location, and even surveillance performed by computers without the need for human input.

Convolutional neural networks and embedded vision are changing the world around us and making interpretation of real-world data significantly easier and faster. If you think this technology can be adapted to improve your project or work, check out our wide array of machine vision cameras for seamless integration and various applications.