High-speed machine vision is increasing its importance in both scientific and technological applications. Neuro-inspired photonic computing is a promising approach to speed-up machine vision processing with ultralow latency. However, the processing rate is fundamentally limited by the low frame rate of image sensors, typically operating at tens of hertz. Here, we propose an image-sensor-free machine vision framework, which optically processes real-world visual information with only a single input channel, based on a random temporal encoding technique. This approach allows for compressive acquisitions of visual information with a single channel at gigahertz rates, outperforming conventional approaches, and enables its direct photonic processing using a photonic reservoir computer in a time domain. We experimentally demonstrate that the proposed approach is capable of high-speed image recognition and anomaly detection, and furthermore, it can be used for high-speed imaging. The proposed approach is multipurpose and can be extended for a wide range of applications, including tracking, controlling, and capturing sub-nanosecond phenomena.

翻译：高速机器视觉在科学和技术应用中的重要性日益凸显。神经启发光子计算是一种以极低延迟加速机器视觉处理的有前景方法。然而，其处理速率从根本上受限于图像传感器通常仅为数十赫兹的低帧率。本文提出一种无图像传感器的机器视觉框架，该框架基于随机时间编码技术，仅需单输入通道即可光学处理真实世界视觉信息。该方法能以千兆赫兹速率实现单通道视觉信息的压缩采集，性能优于传统方法，并能通过时域光子储层计算直接进行光子处理。我们通过实验证明，该方法能够实现高速图像识别与异常检测，并可用于高速成像。该方案具有多用途性，可扩展应用于跟踪、控制及捕获亚纳秒现象等广泛场景。