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Article

Applying a Deep-Learning-Based Keypoint Detection in Analyzing Surface Nanostructures

by
Shaoxuan Yuan
,
Zhiwen Zhu
,
Jiayi Lu
,
Fengru Zheng
,
Hao Jiang
and
Qiang Sun
*
Materials Genome Institute, Shanghai University, Shanghai 200444, China
*
Author to whom correspondence should be addressed.
Molecules 2023, 28(14), 5387; https://doi.org/10.3390/molecules28145387
Submission received: 15 June 2023 / Revised: 9 July 2023 / Accepted: 11 July 2023 / Published: 13 July 2023
(This article belongs to the Special Issue On-Surface Chemical Reactions)
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Abstract

Scanning tunneling microscopy (STM) imaging has been routinely applied in studying surface nanostructures owing to its capability of acquiring high-resolution molecule-level images of surface nanostructures. However, the image analysis still heavily relies on manual analysis, which is often laborious and lacks uniform criteria. Recently, machine learning has emerged as a powerful tool in material science research for the automatic analysis and processing of image data. In this paper, we propose a method for analyzing molecular STM images using computer vision techniques. We develop a lightweight deep learning framework based on the YOLO algorithm by labeling molecules with its keypoints. Our framework achieves high efficiency while maintaining accuracy, enabling the recognitions of molecules and further statistical analysis. In addition, the usefulness of this model is exemplified by exploring the length of polyphenylene chains fabricated from on-surface synthesis. We foresee that computer vision methods will be frequently used in analyzing image data in the field of surface chemistry.
Keywords: YOLO; keypoint recognition; computer vision; scanning tunneling microscope YOLO; keypoint recognition; computer vision; scanning tunneling microscope

Share and Cite

MDPI and ACS Style

Yuan, S.; Zhu, Z.; Lu, J.; Zheng, F.; Jiang, H.; Sun, Q. Applying a Deep-Learning-Based Keypoint Detection in Analyzing Surface Nanostructures. Molecules 2023, 28, 5387. https://doi.org/10.3390/molecules28145387

AMA Style

Yuan S, Zhu Z, Lu J, Zheng F, Jiang H, Sun Q. Applying a Deep-Learning-Based Keypoint Detection in Analyzing Surface Nanostructures. Molecules. 2023; 28(14):5387. https://doi.org/10.3390/molecules28145387

Chicago/Turabian Style

Yuan, Shaoxuan, Zhiwen Zhu, Jiayi Lu, Fengru Zheng, Hao Jiang, and Qiang Sun. 2023. "Applying a Deep-Learning-Based Keypoint Detection in Analyzing Surface Nanostructures" Molecules 28, no. 14: 5387. https://doi.org/10.3390/molecules28145387

APA Style

Yuan, S., Zhu, Z., Lu, J., Zheng, F., Jiang, H., & Sun, Q. (2023). Applying a Deep-Learning-Based Keypoint Detection in Analyzing Surface Nanostructures. Molecules, 28(14), 5387. https://doi.org/10.3390/molecules28145387

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