What Will the Future of Embedded Vision Look Like?

The future of embedded vision technology looks bright. While embedded vision systems were only recently enabled through the miniaturization of cameras and processors, there’s enormous commercial potential for compact vision and imaging.

From the automotive sector to robotics and consumer electronics, embedded vision can be used in many different ways for a variety of functions. In this way, the future of embedded vision technology could take several different paths, but there are a few definitive ways it’s expected to mature.

Embedded Vision in 3D Perception and Deep Learning

It’s clear the next frontier in embedded vision involves the combination of 3D perception and deep learning. These two technologies, enabled by embedded vision capabilities, are only just beginning to integrate but will one day enable vision applications that are currently far from our reach.

As embedded vision facilitates the growth of 3D perception and deep learning, processors will need to continue gaining computing power without sacrificing energy-efficiency or costs to enable powerful deep learning. Simultaneously, the productivity of software development in deep learning initiatives must improve while deep learning capabilities advance for effective deployment in computer vision applications.

ROI Calculator

Discover the potential cost savings of robotic automation over a 20-year system life

This calculator compares your current manual labor costs against the total cost of owning and operating a robotic system over its 20-year lifespan.

EXPLORE TODAY

3D Perception and Deep Learning Applications

While 3D vision and deep learning currently exist, they haven’t yet matured to their full potential. As this maturation happens, applications such as object detection, classification and semantic segmentation become possible, which have a number of commercial uses.

Embedded vision, using 3D perception and deep learning, will lead to highly flexible and intelligent robots in factory settings. These robots could instantly detect new parts and learn how to handle them, theoretically allowing them to perform entirely new tasks with little to no instruction.

These technologies also contribute to visual simultaneous localization and mapping (SLAM) capabilities, which are an essential part of autonomous operations in cars, trucks, robots, drones and more.

The combination of deep learning and 3D perception will depend heavily on embedded vision technology and is one of the clearest examples of what lies ahead for embedded vision.

While embedded vision is still relatively new, the future is bright. Deep learning and 3D perception will be important technological advances, but far from the only way that embedded vision will transform entire industries.