Acta Optica Sinica, Volume. 42, Issue 19, 1912006(2022)
Particle Size and Position Measurement of Defocused Particle Based on Convolutional Neural Network
Fig. 1. Principle diagram of dual-camera defocused imaging
Fig. 2. Faster-RCNN network architecture[19]
Fig. 3. VGG16 network architecture[20]
Fig. 4. Flow chart of simultaneous particle size and position prediction based on convolution neural network
Fig. 5. Schematic diagram of dual-camera system and calibration test bench[17]. (a) Schematic diagram of dual-camera system; (b) calibration test bench
Fig. 6. Partial dot images after cropping
Fig. 7. Comparison of particle size and depth errors under different particle sizes. (a)(b) particle size error; (c)(d) depth error
Fig. 8. Size prediction error of standard particles in circulating sample cell
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Xiangyun Zhang, Wu Zhou, Youxin Jiang, Xiangxuejie Xia. Particle Size and Position Measurement of Defocused Particle Based on Convolutional Neural Network[J]. Acta Optica Sinica, 2022, 42(19): 1912006
Category: Instrumentation, Measurement and Metrology
Received: Feb. 28, 2022
Accepted: Apr. 16, 2022
Published Online: Oct. 18, 2022
The Author Email: Zhou Wu (zhouwu@usst.edu.cn)