化学実験画像データセットの作成と物体検出の数値検証
In recent years, the remarkable advances in artificial intelligence technology have led to digital transformation (DX) in various fields. The automated construction of laboratory notebook through filming experiments is a promising application of image recognition for chemistry． In this study, we cre...
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| Published in | Journal of Computer Chemistry, Japan Vol. 21; no. 2; pp. 58 - 60 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | Japanese |
| Published |
日本コンピュータ化学会
2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1347-1767 1347-3824 |
| DOI | 10.2477/jccj.2022-0025 |
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| Abstract | In recent years, the remarkable advances in artificial intelligence technology have led to digital transformation (DX) in various fields. The automated construction of laboratory notebook through filming experiments is a promising application of image recognition for chemistry． In this study, we created an image dataset of chemical experiment, which contains 2376 images and consists of 7 classes of objects. Object detection methods and a multiple object tracking method were implemented and assessed using the dataset toward to develop automated laboratory notebook system. |
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| AbstractList | In recent years, the remarkable advances in artificial intelligence technology have led to digital transformation (DX) in various fields. The automated construction of laboratory notebook through filming experiments is a promising application of image recognition for chemistry． In this study, we created an image dataset of chemical experiment, which contains 2376 images and consists of 7 classes of objects. Object detection methods and a multiple object tracking method were implemented and assessed using the dataset toward to develop automated laboratory notebook system. |
| Author | 藤波, 美起登 佐々木, 良輔 中井, 浩巳 |
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| Copyright | 2022 日本コンピュータ化学会 |
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| DOI | 10.2477/jccj.2022-0025 |
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| References | [1] S． Rostianingsih, A． Setiawan, C． I． Halim, Procedia Comput. Sci., 171, 2445 (2020)．doi:10.1016/j.procs.2020.04.264 [4] M． Everingham, L． Van Gool, C． K． I． Williams, J． Winn, A． Zisserman, Int. J. Comput. Vis., 88, 303 (2010).doi:10.1007/s11263-009-0275-4 [9] Github, https://github．com/eriklindernoren/PyTorch-YOLOv3, (accessed on 2022/08/17) [2] Z． Ding, S． Ran, Z． Wu, Z． He, Q． Chen, Y． Wei, et al．, Artificial Intelligence in Data and Big Data Processing． ICABDE 2021． Springer, 201 (2022) [6] J． Redmon, A． Farhadi, arXiv:1804．02767 (2018) [3] Github, https://github．com/microsoft/VoTT (accessed on 2022/08/17) [8] Github, https://github．com/amdegroot/ssd．pytorch (accessed on 2022/08/17) [7] A． Bewley, Z． Ge, L． Ott, F． Ramos, B． Upcroft, 2016 IEEE international conference on image processing (ICIP)． IEEE, 3464 (2016) [11] R． Padilla, S． L． Netto, E． A． B． da Silva, 2020 international conference on systems, signals and image processing (IWSSIP), IEEE, 237 (2020) [5] W． Liu, D． Anguelov, D． Erhan, C． Szegedy, S． Reed, C．-Y． Fu, et al．, ECCV 2016, Springer, 21 (2016) [10] Github, https://github．com/cfotache/pytorch_objectdetecttrack, (accessed on 2022/08/17) |
| References_xml | – reference: [5] W． Liu, D． Anguelov, D． Erhan, C． Szegedy, S． Reed, C．-Y． Fu, et al．, ECCV 2016, Springer, 21 (2016)． – reference: [3] Github, https://github．com/microsoft/VoTT (accessed on 2022/08/17)． – reference: [9] Github, https://github．com/eriklindernoren/PyTorch-YOLOv3, (accessed on 2022/08/17)． – reference: [6] J． Redmon, A． Farhadi, arXiv:1804．02767 (2018)． – reference: [10] Github, https://github．com/cfotache/pytorch_objectdetecttrack, (accessed on 2022/08/17)． – reference: [1] S． Rostianingsih, A． Setiawan, C． I． Halim, Procedia Comput. Sci., 171, 2445 (2020)．doi:10.1016/j.procs.2020.04.264 – reference: [2] Z． Ding, S． Ran, Z． Wu, Z． He, Q． Chen, Y． Wei, et al．, Artificial Intelligence in Data and Big Data Processing． ICABDE 2021． Springer, 201 (2022)． – reference: [11] R． Padilla, S． L． Netto, E． A． B． da Silva, 2020 international conference on systems, signals and image processing (IWSSIP), IEEE, 237 (2020)． – reference: [7] A． Bewley, Z． Ge, L． Ott, F． Ramos, B． Upcroft, 2016 IEEE international conference on image processing (ICIP)． IEEE, 3464 (2016)． – reference: [4] M． Everingham, L． Van Gool, C． K． I． Williams, J． Winn, A． Zisserman, Int. J. Comput. Vis., 88, 303 (2010).doi:10.1007/s11263-009-0275-4 – reference: [8] Github, https://github．com/amdegroot/ssd．pytorch (accessed on 2022/08/17)． |
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| SubjectTerms | Chemical experiment Laboratory notebook Machine learning Multiple object tracking Object detection |
| Title | 化学実験画像データセットの作成と物体検出の数値検証 |
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