Machine vision based Vernier caliper reading technology research
DOI:
https://doi.org/10.24425/mms.2022.143070Abstract
Accurate and fast access to Vernier caliper readings is a critical issue in automated verification of Vernier calipers. To address this problem, this paper proposes a machine vision-based algorithm for reading the Vernier caliper’s displayed value. The suggested method first employs threshold segmentation and template matching to determine the region of interest and obtain the main ruler digit position by alternate projection. Then, we apply the improved LeNet5 network to identify the main ruler of the Vernier caliper, Moreover, we developed the first and last inscription method for reading the decimal part of the Vernier caliper and established our data set for model training. Extensive experiments on reading the displayed value have demonstrated our algorithm’s accuracy, which achieves a displayed value reading accuracy of 100%. Compared to other methods, the proposed technique affords better stability and accuracy.References
[1] Shirmohammadi, S., & Ferrero. A. (2014). Camera as the instrument: the rising trend of vision based measurement. IEEE Instrumentation Measurement Magazine, 17(3), 41-47. https://doi.org/10.1109/MIM.2014.6825388
[2] Borwankar, R., & Ludwig, R. (2020). A novel compact convolutional neural network for real-time non-destructive evaluation of metallic surfaces. IEEE Transactions on Instrumentation and Measurement, 69(10), 8466-8473. https://doi.org/10.1109/TIM.2020.2990541
[3] Liu, Y., Gao, H., Guo, L., Qin, A., Cai, C., & You, Z. (2019). A data-flow oriented deep ensemble learning method for real-time surface defect inspection. IEEE Transactions on Instrumentation and Measurement, 69(7), 4681-4691. https://doi.org/10.1109/TIM.2019.2957849
[4] Li, X., Yang, Y., Ye, Y., Ma, S., & Hu, T. (2021). An online visual measurement method for workpiece dimension based on deep learning. Measurement, 185, 110032. https://doi.org/10.1016/j.measurement.2021.110032
[5] Yu. J., Cheng, X., Lu, L., & Wu. B. (2021). A machine vision method for measurement of machining tool wear. Measurement, 182, 109683. https://doi.org/10.1016/j.measurement.2021.109683
[6] Yang, Y., Wu, H., Wang, P., & Yang. F. (2020). Substation pointer meters detection and reading based on CNN. International Symposium on Artificial Intelligence and Robotics 2020. https://doi.org/10.1117/12.2575960
[7] Zhang, X.. Dang, X., Lv, Q., & Liu, S. (2020. April). A pointer meter recognition algorithm based on deep learning. In 2020 3rd International Conference on Advanced Electronic Materials. Computers and Software Engineering (AEMCSE) (pp, 283-287). IEEE. https://doi.org/10.1109/AEMCSE50948.2020.00068
[8] Zuo, L., He, P., Zhang. C., & Zhang, Z. (2020). A robust approach to reading recognition of pointer meters based on improved mask-RCNN. Neurocomputing, 388, 90-101. https://doi.org/10.1016/j.neucom.2020.01.032
[9] Liu, Y., Liu. J., & Ke, Y. (2020). A detection and recognition system of pointer meters in substations based on computer vision. Measurement, 152, 107333. https://doi.org/10.1016/j.measurement.2019.107333
[10] Zhou. D., Yang, Y., Zhu. J., & Wang, K. (2022). Intelligent reading recognition method of a pointer meter based on deep learning in a real environment. Measurement Science and Technology, 33(3), 055021. https://doi.org/10.1088/1361-6501/ac4079
[11] Chengde, B., Zhiling, X., Pengfeng, W., Wangda, C., & Lei, Y. (2016). Design of a device for the indication verification of Vernier calipers. Journal of China University of Metrology, 27(02), 138-143. https://doi.org/10.3969/j.issn.1004-1540.2016.02.003 (in Chinese)
[12] Mengpei, W., & Houyun, Y. (2019). Research on Automatic Verification Method of Vernier Caliper Indication Error. Machine Building & Automation, 49(6), 203-205. https://doi.org/10.19344/j.cnki.issn1671-5276.2020.06.053 (in Chinese)
[13] Shuang, D., Guo-ying, R., Fu-min, Z., & Nai-yin, F. (2019). Improved Threading Method for Caliper Image Recognition, 40(3), 765-769. https://doi.org/10.3969/j.issn.1000-1158.2019.05.04 (in Chinese)
[14] Houyun, Y., Huiqing, W., Wei, W., & Yue, J. (2020). Identification of Numbers for Vernier Caliper Automatic Verfication System Based on Convolution Neural Network. Chinese Journal of Sensors and Actuators, 33(12), 1718-1726. https://doi.org/10.3969/j.issn.1004-1699.2020.12.007 (in Chinese)
[15] Hashemi, N. S., Aghdam, R. B., Ghiasi, A. S. B., & Fatemi, P. (2016). Template matching advances and applications in image analysis. arXiv preprint arXiv:1610.07231. https://doi.org/10.48550/arXiv.1610.07231
[16] Otsu, N. (1979). A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems Man & Cybernetics, 9(1), 62-66. https://doi.org/10.1109/TSMC.1979.4310076
[17] Yi-yan, S., Dong-lin, T., Xu-long, W., Li, Z., & Bei-xuan, Q. (2021). Study on Digital Tube Image Reading Combining Improved Threading Method with HOG+SVM Method. Computer Science, 48(11A), 396-399. https://doi.org/10.11896/jsjkx.210100123
[18] Ahlawat, S., Choudhary. A., Nayyar, A., Singh, S., & Yoon, B. (2020). Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN). Sensors, 20(12), 3344. https://doi.org/10.3390/s20123344
[19] Salemdeeb, M., & Erturk, S. (2021). Full depth CNN classifier for handwritten and license plate characters recognition. Peerj Computer Science, 7, e576. https://doi.org/10.7717/peerj-cs.576
[20] Lecun, Y., & Bottou, L. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. https://doi.org/10.1109/5.726791
[21] Ide, H., & Kurita, T. (2017, May). Improvement of learning for CNN with ReLU activation by sparse regularization. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 2684-2691). IEEE. https://doi.org/10.1109/IJCNN.2017.7966185
[22] Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R, R. (2012). Improving neural networks by preventing co-adaptation of feature detectors, arXiv preprint arXiv:1207.0580. https://doi.org/10.48550/arXiv.1207.0580
[23] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15(1), 1929-1958. https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf
[24] Wangda. C., Zhiling, X., & Zhifei, L. (2019). Design of Indication Verification Device for External Diameter Micrometer Based on Machine Vision. Machine Tool & Hydraulics, 47(17), 115-119. https://doi.org/10.3969/j.issn.1001-3881.2019.17.021 (in Chinese)
