Zhang Zibang, Li Xiang, Zheng Shujun, Yao Manhong, Zheng Guoan, Zhong Jingang
Opt Express. 2020 Apr 27;28(9):13269-13278. doi: 10.1364/OE.392370.
Object classification generally relies on image acquisition and subsequent analysis. Real-time classification of fast-moving objects is a challenging task. Here we propose an approach for real-time classification of fast-moving objects without image acquisition. The key to the approach is to use structured illumination and single-pixel detection to acquire the object features directly. A convolutional neural network (CNN) is trained to learn the object features. The "learned" object features are then used as structured patterns for structured illumination. Object classification can be achieved by picking up the resulting light signals by a single-pixel detector and feeding the single-pixel measurements to the trained CNN. In our experiments, we show that accurate and real-time classification of fast-moving objects can be achieved. Potential applications of the proposed approach include rapid classification of flowing cells, assembly-line inspection, and aircraft classification in defense applications. Benefiting from the use of a single-pixel detector, the approach might be applicable for hidden moving object classification.
目标分类通常依赖于图像采集及后续分析。对快速移动目标进行实时分类是一项具有挑战性的任务。在此，我们提出一种无需图像采集即可对快速移动目标进行实时分类的方法。该方法的关键在于利用结构化照明和单像素检测直接获取目标特征。训练一个卷积神经网络（CNN）来学习目标特征。然后将“学到的”目标特征用作结构化照明的结构化图案。通过单像素探测器拾取产生的光信号，并将单像素测量值输入到经过训练的CNN中，即可实现目标分类。在我们的实验中，我们表明能够实现对快速移动目标的准确实时分类。该方法的潜在应用包括流动细胞的快速分类、装配线检测以及国防应用中的飞机分类。受益于单像素探测器的使用，该方法可能适用于隐藏移动目标的分类。
