Engineering (Jun 2023)

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

Journal volume & issue
Vol. 25
pp. 110 – 119

Abstract

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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 vform, 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 spike camera that is 1000× faster than conventional cameras. By treating vform 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 spike 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.

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