Design and Implementation of an Automatic White Sugar Impurity Detection System based on Multispectral Imaging

Authors

  • Xin Zhong
  • Li Luo
  • Xiangyang Yu
  • Yefan Cai
  • Weibing Hong

DOI:

https://doi.org/10.54097/36j24175

Keywords:

Multi-modal Image Fusion, Attention Unet, Multispectral Images, Impurity Detection, Deep Learning

Abstract

Traditional methods for removing impurities from sugar are not very inefficient and have limited accuracy. This study designed and implemented an automatic white sugar impurity detection system based on multi-modal image fusion technology to detect impurities in white sugar efficiently and accurately. The system utilizes 4 multi-spectral cameras and an RGB camera to acquire high-resolution images of the samples. By extending the input channels of the Attention U-Net model to thirteen, the recognition rate of impurity categories is improved by approximately 60%, significantly enhancing the detection performance of the model. The experimental results show that the system performs excellently in the image segmentation task, achieving an average intersection over union (mIoU) of 92.70% and an average pixel accuracy (mPA) of 93.74%. The accuracy for impurity categories reaches 95%. Finally, the model was applied to test the effectiveness in identifying impurities; the results indicate that the model can effectively identify various types of impurities and also perform well in recognizing unknown categories.

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References

[1] Müller, H., & Agustín, P. (2021). A review of traditional food quality control methods and their limitations. Food Chemistry, 332, 127373. DOI: 10.1016/j.foodchem.2020.127373.

[2] World Health Organization. Guideline: sugars intake for adults and children[M]. World Health Organization, 2015.

[3] Jethi G S, Sunori S K, Joshi P, et al. Application of Machine Learning in Assessing the Sugar Quality in Sugar Mills[C]// 2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV). IEEE, 2024: 167-172.

[4] Wang, Y. H., Zhao, D. D., & Li, Q. T. (2014). Rapid analysis of molasses hammer degree, sucrose content, and reducing sugar content by near-infrared spectroscopy [in Chinese]. Food Science and Technology, 39(9), 284-288.

[5] HASHIM N, ONWUDE D I, OSMAN M S. Evaluation of chilling injury in mangoes using multispectral imaging[J]. Journal of Food Science, 2018, 83(5): 1271-1279.

[6] Huang, Z., Chen, J., Tan, G., et al. (2024). Hyperspectral non-tobacco material detection method based on multi-type convolutional mixing [in Chinese]. Journal of Image and Signal Processing, 13, 76.

[7] Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (200˜). LabelMe: A Database and Web-Based Tool for Image Annotation. International Journal of Computer Vision, 77(1-3), 157-173.

[8] Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 234-241. Springer, Cham. DOI: 10. 1007/ 978-3-319-24574-4_28.

[9] Oktay, O., Schlemper, J., Folgoc, L. L., et al. (2018). Attention U-Net: Learning Where to Look for the Pancreas. arXiv preprint arXiv:1804.03999.

[10] Wang, X., & Guo, X. (2020). Attention U-Net Based on Residual Learning for Medical Image Segmentation. IEEE Access, 8, 55230-55239. DOI: 10.1109/ ACCESS. 2020. 298 2408.

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Published

21-01-2025

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Section

Articles

How to Cite

Zhong, X., Luo, L., Yu, X., Cai, Y., & Hong, W. (2025). Design and Implementation of an Automatic White Sugar Impurity Detection System based on Multispectral Imaging. Frontiers in Computing and Intelligent Systems, 11(1), 5-11. https://doi.org/10.54097/36j24175