High-accuracy image-free classification of high-speed rotating objects with fluctuating rotation periods

Abstract

Image-free classification methods with single-pixel measuring and deep learning show a capacity for long-duration classification of moving objects. However, motion blur restricts the allowable object motion speed of existing image-free classification methods. Aimed at high-speed rotating objects, we propose an image-free classification approach based on single-pixel measuring at the same spatial position of different rotation cycles to reduce motion blur. The proposed approach allows classifying rotating objects with fluctuating rotation periods to better meet the actual application conditions. We verify the proposed method by classifying the digits placed on a rotating disk. In our experiments, when digits rotate at around 960.9 revolutions per minute, corresponding to 10.06 m/s, the classification accuracy reaches 95.9%. In theory, the allowable speed is determined only by the sampling rate of the single-pixel measurements, which can allow for higher speeds than experimentally achieved. The proposed image-free classification method provides a promising way of monitoring high-speed rotating objects in the engineering field.