This Compact Camera Can Identify Objects at Speed of Light

Scientists developed a new type of compact camera engineered for computer vision. Their prototype (shown above) uses optics for computing, significantly reducing power consumption and enabling the camera to identify objects at the speed of light.

A pair of scientists have developed a compact camera that can identify objects at the speed of light.

Arka Majumdar, a University Washington professor in electrical and computer engineering and physics, and Felix Heide, an assistant professor of computer science at Princeton University, developed a new type of compact camera where the lens is replaced with engineered optics that can identify objects at lightspeed.

The two scientists outlined their new invention in a recent paper published in Science Advances.

In the paper, Majumdar and Heide describe a new type of compact camera engineered for computer vision — a type of AI that enables computers to recognize objects in images and video.

According to a news release by the University of Washington, the pair’s research prototype uses optics for computing, significantly reducing power consumption and allowing the camera to identify objects at the speed of light, representing a new approach to the field of computer vision.

“This is a completely new way of thinking about optics, which is very different from traditional optics. It’s end-to-end design, where the optics are designed in conjunction with the computational block,” Majumdar says. “Here, we replaced the camera lens with engineered optics, which allows us to put a lot of the computation into the optics.”

“There are really broad applications for this research, from self-driving cars, self-driving trucks, and other robotics to medical devices and smartphones. Nowadays, every iPhone has AI or vision technology in it,” Heide adds. “This work is still at a very early stage, but all of these applications could someday benefit from what we are developing.”

Instead of using a traditional camera lens made out of glass or plastic, the optics in this camera relies on layers of 50 meta-lenses – flat, lightweight optical components that use microscopic nanostructures to manipulate light. These meta-lenses fit into a compact, optical computing chip (shown above).

Instead of using a traditional camera lens made out of glass or plastic, the optics in this camera relies on layers of 50 meta-lenses — flat, lightweight optical components that use microscopic nanostructures to manipulate light.

The meta-lenses also function as an optical neural network, which is a computer system that is a form of artificial intelligence modeled on the human brain. This unique approach has a couple of key advantages. Firstly, it’s fast because much of the computation takes place at the speed of light, the system can identify and classify images more than 200 times faster than neural networks that use conventional computer hardware, and with comparable accuracy.

Secondly, because the optics in the camera rely on incoming light to operate, rather than electricity, the power consumption is greatly reduced.

Heide and his students at Princeton University provided the design for the camera prototype, which is a compact, optical computing chip. Majumdar contributed his expertise in optics to help engineer the camera, and he and his students fabricated the chip in the Washington Nanofabrication Laboratory.

In their previous research, Heide and Majumdar developed a high-resolution, full-color camera that is the size of a grain of salt.

Image credits: All photos by Ilya Chugunov, courtesy of Princeton University via University of Washington.