1000× Faster Camera and Machine Vision with Ordinary Devices
Tiejun Huang , Yajing Zheng , Zhaofei Yu , Rui Chen , Yuan Li , Ruiqin Xiong , Lei Ma , Junwei Zhao , Siwei Dong , Lin Zhu , Jianing Li , Shanshan Jia , Yihua Fu , Boxin Shi , Si Wu , Yonghong Tian
Engineering ›› 2023, Vol. 25 ›› Issue (6) : 110 -119.
1000× Faster Camera and Machine Vision with Ordinary Devices
In digital cameras, we find a major limitation: the image and video form inherited from a film camera obstructs it from capturing the rapidly changing photonic world. Here, we present vidar, a bit sequence array where each bit represents whether the accumulation of photons has reached a threshold, to record and reconstruct the scene radiance at any moment. By employing only consumer-level complementary metal-oxide-semiconductor (CMOS) sensors and integrated circuits, we have developed a vidar camera that is 1000× faster than conventional cameras. By treating vidar as spike trains in biological vision, we have further developed a spiking neural network (SNN)-based machine vision system that combines the speed of the machine and the mechanism of biological vision, achieving high-speed object detection and tracking 1000× faster than human vision. We demonstrate the utility of the vidar camera and the super vision system in an assistant referee and target pointing system. Our study is expected to fundamentally revolutionize the image and video concepts and related industries, including photography, movies, and visual media, and to unseal a new SNN-enabled speed-free machine vision era.
Vidar camera / Spiking neural networks / Super vision system / Full-time imaging
()
/
| 〈 |
|
〉 |
PDF
(3410KB)
