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A benchmark dataset in chemical apparatus: recognition and detection

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Abstract

Robots that perform chemical experiments autonomously have been implemented, using the same chemical apparatus as human chemists and capable of performing complex chemical experiments unmanaged. However, most robots in chemistry are still programmed and cannot adapt to diverse environments or to changes in displacement and angle of the object. To resolve this issue, we have conceived a computer vision method for identifying and detecting chemical apparatus automatically. Identifying and localizing such apparatus accurately from chemistry lab images is the most important task. We acquired 2246 images from real chemistry laboratories, with a total of 33,108 apparatus instances containing 21 classes. We demonstrate a Chemical Apparatus Benchmark Dataset (CABD) containing a chemical apparatus image recognition dataset and a chemical apparatus object detection dataset. We evaluated five excellent image recognition models: AlexNet, VGG16, GoogLeNet, ResNet50, MobileNetV2 and four state-of-the-art object detection methods: Faster R-CNN (3 backbones), Single Shot MultiBox Detector (SSD), YOLOv3-SPP and YOLOv5, respectively, on the CABD dataset. The results can serve as a baseline for future research. Experiments show that ResNet50 has the highest accuracy (99.9%) in the chemical apparatus image recognition dataset; Faster R-CNN (ResNet50-fpn) and YOLOv5 performed the best in terms of mAP (99.0%) and AR (94.5%) in the chemical apparatus object detection dataset.

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Data availability

The data that support the findings of this study are openly available in Zenodo,reference number [49].

Notes

  1. An earlier version of this paper was presented at the International conference on Artificial Intelligence and Big Data in Digital Era.

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Acknowledgements

This work was supported by the grant of Anhui Provincial Natural Science Foundation, Nos. 1908085MF184, 1908085QF285, the grant of Scientific Research and Talent Development Foundation of the Hefei University, No.21-22RC15, the Key Research Plan of Anhui Province, Nos. 202104d07020006, 2022 k07020011, the grant of the Hefei University Postgraduate Innovation and Entrepreneurship Project, Nos. 21YCXL16,21YCXL14, in part by the grant of Key Generic Technology Research and Development Project of Hefei, No. 2021GJ030, the grant of Program for Scientific Research Innovation Team in Colleges and Universities of Anhui Province 2022AH010095, as well as the AI General Computing Platform of Hefei University.

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Authors and Affiliations

  1. School of Artificial Intelligence and Big Data, Hefei University, Hefei, 230601, China

    Le Zou, Ze-Sheng Ding, Zhi-Huang He & Xiao-Feng Wang

  2. Institute of Applied Optimization, School of Artificial Intelligence and Big Data, Hefei University, Hefei, 230601, Anhui, China

    Shuo-Yi Ran & Zhi-Ze Wu

  3. School of Energy Materials and Chemical Engineering, Hefei University, Hefei, 230601, Anhui, China

    Yun-Sheng Wei

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Zou, L., Ding, ZS., Ran, SY. et al. A benchmark dataset in chemical apparatus: recognition and detection. Multimed Tools Appl 83, 26419–26437 (2024). https://doi.org/10.1007/s11042-023-16563-8

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