[Published online Journal of Computer Chemistry, Japan Vol.21, 58-60, by J-STAGE]
<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
<Author(s):> 佐々木 良輔, 藤波 美起登, 中井 浩巳
<Corresponding author E-Mill:> nakai(at)waseda.jp
<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.
<Keywords:> Machine learning, Object detection, Multiple object tracking, Laboratory notebook, Chemical experiment
<URL:> https://www.jstage.jst.go.jp/article/jccj/21/2/21_2022-0025/_article/-char/ja/
<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
<Author(s):> 佐々木 良輔, 藤波 美起登, 中井 浩巳
<Corresponding author E-Mill:> nakai(at)waseda.jp
<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.
<Keywords:> Machine learning, Object detection, Multiple object tracking, Laboratory notebook, Chemical experiment
<URL:> https://www.jstage.jst.go.jp/article/jccj/21/2/21_2022-0025/_article/-char/ja/
