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Deep-learning-based image segmentation integrated with optical microscopy for automatically searching for two-dimensional materials

Engineering and Technology

Deep-learning-based image segmentation integrated with optical microscopy for automatically searching for two-dimensional materials

S. Masubuchi, E. Watanabe, et al.

This innovative research conducted by Satoru Masubuchi and colleagues showcases a deep-learning-based image segmentation algorithm that integrates seamlessly with an autonomous robotic system, revolutionizing the automated search and cataloging of 2D materials. With the robust Mask-RCNN neural network and advanced microscopy, this technology promises to enhance efficiency in 2D material research like never before.

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Playback language: English
Abstract
This paper presents a deep-learning-based image segmentation algorithm integrated with an autonomous robotic system for the automated search and cataloging of two-dimensional (2D) materials. A Mask-RCNN neural network, trained on annotated optical microscope images of various 2D materials, enables real-time detection with robustness against variations in microscopy conditions. The system's integration with a motorized optical microscope facilitates high-throughput searching and contributes to efficient 2D material research.
Publisher
npj 2D Materials and Applications
Published On
Mar 23, 2020
Authors
Satoru Masubuchi, Eisuke Watanabe, Yuta Seo, Shota Okazaki, Takao Sasagawa, Kenji Watanabe, Takashi Taniguchi, Tomoki Machida
Tags
image segmentation
deep learning
autonomous robotics
2D materials
Mask-RCNN
microscopy
high-throughput

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