Mr. Yuichiro Kunai Profile

Yuichiro Kunai

at Fujikura Ltd

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Publications (2)

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Proceedings Article | 28 May 2024 Presentation + Paper

High accuracy object detection by an optical neural network implementation

Mamoru Otake, Shun Miura, Hiroyuki Kusaka, Masahiro Kashiwagi, Yuichiro Kunai, Takahiro Nambara, Yumi Yamada

Proceedings Volume 13083, 1308303 (2024) https://doi.org/10.1117/12.3020003

KEYWORDS: Object detection, Target detection, Neural networks, Spatial light modulators, Education and training, Phase modulation, Simulations

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In recent years, AI technology using Neural Network (NN) has made remarkable progress and is used for highly accurate classification, object detection, and anomaly detection in sensing. The difficulties with high-accuracy NN are the long processing time and high-power consumption. As one solution, an optical neural network (ONN), which realizes NN by diffraction and propagation of light, has attracted attention as an implementation method with ultra-high speed and low power consumption. Although many of the prior studies on ONN are related to classification, ONN has the potential to be applied to various tasks. As one example, the use of ONN has the possibility of ultra-fast object detection. In this study, simulations and experiments were conducted to verify the possibility of detection by ONN. Metal nuts were selected as the detection targets as a representative example of mass-produced industrial parts. In the experiment, SLM was used to implement the data input layer as phase input and the trained diffraction layer. First, the case of a single detection target in the input data was demonstrated. The precision for the 551-input data was 96.4 % in the experiment. In the data that could be detected correctly, the root mean square error between the inferred and correct positions was 2.2 % of the metal nut size. Next, another experiment has confirmed that ONN can detect multiple targets accurately. In addition, we examined ONN that uses light transmitted through the sample and found that the inference process finished within 4.17 msec (the response time of the CMOS of this setup). The results show that ONN can accurately and rapidly detect objects.

Proceedings Article | 22 May 2023 Presentation + Paper

High accuracy inference by an optical neural network implementation

Shun Miura, Mamoru Otake, Hiroyuki Kusaka, Masahiro Kashiwagi, Yuichiro Kunai, Takahiro Nambara, Yumi Yamada

Proceedings Volume 12327, 123270E (2023) https://doi.org/10.1117/12.2643679

KEYWORDS: Simulations, Neural networks, Mirrors, Spatial light modulators, Reflection, Image processing, Phase modulation, Neurons, Wavefronts

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In recent years, sensing and imaging have significantly progressed due to AI algorithms such as Neural Network (NN). The main issues of applying NNs to information processing are the limited processing speed and high energy consumption of electronic processors. Optical Neural Network (ONN), which utilizes diffraction and propagation of light for processing, is an intriguing implementation of an ultra-fast and low-energy-consuming NN. However, previous studies of ONN are mainly on simulations due to the experimental difficulty of processing more than hundreds of input data. In hardware implementations, the performance or the classification accuracy of ONNs can be reduced by the noise and the displacements. Therefore, not only must the ONN achieve high theoretical accuracy, but it must also be robust to these experimental errors. In this study, the classification of 1,000 MNIST input data (100 data for each of 10 classes) was realized experimentally as well as theoretically, taking advantage of our novel setup with a variable spatial light modulator (SLM). With our experimental configuration, we investigated the classification accuracy with several loss functions for the ONN training. The inference accuracy of the MNIST classification task was up to 97% in the simulation and ~95% in the experiment by softmax-cross-entropy (SCE) loss function. Also, the classification accuracy of 98% for a Surface crack classification and 93% for a Pollen classification was achieved experimentally. These results show that SCE can realize high-accuracy classification in the ONN implementation. Our results revealed the high application capability of the optical neural network for practical sensing tasks.

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