Image-free classification of fast-moving objects using 'learned' structured illumination and single-pixel detection
| Autor: | Jingang Zhong, Manhong Yao, Zibang Zhang, Guoan Zheng, Xiang Li, Shujun Zheng |
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| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
business.industry Computer science Image quality Detector ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Structured illumination microscopy 02 engineering and technology Ghost imaging 021001 nanoscience & nanotechnology Object (computer science) 01 natural sciences Convolutional neural network Atomic and Molecular Physics and Optics 010309 optics Light intensity Optics 0103 physical sciences Key (cryptography) Computer vision Artificial intelligence 0210 nano-technology business |
| Zdroj: | Optics express. 28(9) |
| ISSN: | 1094-4087 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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