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Deep-learning-based image segmentation integrated with optical microscopy for automatically searching for two-dimensional materials
- Source :
- npj 2D Materials and Applications, Vol 4, Iss 1, Pp 1-9 (2020)
- Publication Year :
- 2020
- Publisher :
- Nature Publishing Group, 2020.
-
Abstract
- Deep-learning algorithms enable precise image recognition based on high-dimensional hierarchical image features. Here, we report the development and implementation of a deep-learning-based image segmentation algorithm in an autonomous robotic system to search for two-dimensional (2D) materials. We trained the neural network based on Mask-RCNN on annotated optical microscope images of 2D materials (graphene, hBN, MoS2, and WTe2). The inference algorithm is run on a 1024 × 1024 px2 optical microscope images for 200 ms, enabling the real-time detection of 2D materials. The detection process is robust against changes in the microscopy conditions, such as illumination and color balance, which obviates the parameter-tuning process required for conventional rule-based detection algorithms. Integrating the algorithm with a motorized optical microscope enables the automated searching and cataloging of 2D materials. This development will allow researchers to utilize a large number of 2D materials simply by exfoliating and running the automated searching process. To facilitate research, we make the training codes, dataset, and model weights publicly available.
- Subjects :
- ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Inference
Color balance
FOS: Physical sciences
02 engineering and technology
law.invention
lcsh:Chemistry
03 medical and health sciences
Optical microscope
law
Microscopy
FOS: Electrical engineering, electronic engineering, information engineering
lcsh:TA401-492
General Materials Science
Computer vision
030304 developmental biology
0303 health sciences
Condensed Matter - Materials Science
Artificial neural network
business.industry
Mechanical Engineering
Deep learning
Image and Video Processing (eess.IV)
Process (computing)
Materials Science (cond-mat.mtrl-sci)
General Chemistry
Image segmentation
Electrical Engineering and Systems Science - Image and Video Processing
021001 nanoscience & nanotechnology
Condensed Matter Physics
lcsh:QD1-999
Mechanics of Materials
lcsh:Materials of engineering and construction. Mechanics of materials
Artificial intelligence
0210 nano-technology
business
Subjects
Details
- Language :
- English
- ISSN :
- 23977132
- Volume :
- 4
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- npj 2D Materials and Applications
- Accession number :
- edsair.doi.dedup.....7bd655801ba081c3287407a743155dd4