Infrared and Laser Engineering, Volume. 50, Issue 12, 20210856(2021)
Single-pixel fast-moving object classification based on optical-electronical hybrid neural network (Invited)
Fig. 1. Optical configuration of structured detected single-pixel imaging
Fig. 4. Example of the original training images and corresponding images with random rotation and lateral shift
Fig. 5. Confusion matrix of the classification results on handwritten digit test set (15 kernels)
Fig. 6. 2D convolutional kernel images of the first layer in the fully convolutional neural network
Fig. 7. MNIST test set classification accuracy of networks with different number of convolutional kernels
Fig. 8. Optical system. (a) Experimental setup; (b) Layout of the handwritten digits on disk
Fig. 10. Snapshots of digit "5" in motion at different speeds captured by using a camera
Fig. 11. Single-pixel measurements of moving handwritten digits. (a) Single-pixel measurements of handwritten digits passing through the field of view successively in 1.5 s; (b) Partially enlarged view of the single-pixel measurements of the digit "5" in (a); (c) Result of the differential measurement from (b)
Fig. 12. The ten classes and example images in Fashion-MINST dataset
Fig. 13. Fashion-MINST test set classification accuracy of networks with different number of convolutional kernels
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Shujun Zheng, Manhong Yao, Shengping Wang, Zibang Zhang, Junzheng Peng, Jingang Zhong. Single-pixel fast-moving object classification based on optical-electronical hybrid neural network (Invited)[J]. Infrared and Laser Engineering, 2021, 50(12): 20210856
Category: Special issue—Single-pixel imaging
Received: Nov. 16, 2021
Accepted: --
Published Online: Feb. 9, 2022
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