Abstract
Laser lines emitted by the laser level are mostly detected manually and laser particle and optical effects also bring difficulties on measurement. In this paper, we design a detection system for the five-line laser level and propose a laser line measurement method based on ma- chine vision. Image processing is divided into two stages: in the first stage, we use random sample consensus (RANSAC) algorithm combined with Hough transform to fit the laser axis, which can get its position information. In the second stage, a laser edge extraction method based on conditional random fields (CRFs) is proposed, and the sub-pixel width of laser line is obtained by spline interpolation algorithm. The results confirm that the laser level detection method proposed in this paper can realize the corresponding detection precision and requirement.
This work is supported by Key Project of Science and Technology Commission of Shanghai Municipality under Grant No. 14JC1402200
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References
Xie, D.: Line-width measurement method of narrow line width laser. Laser Optoelectron. Progress 50(1), 61–66 (2013)
Yue, Y.L., Qin, B., Hong-Wei, L.V., et al.: Comparison of several ultra-narrow laser linewidth measurement. Opt. Commun. Technol. 37(3), 19–21 (2013)
An, P., Zhao, R., Zheng, Y., et al.: Linewidth rapid measurement of narrow fiber laser by spectrum analyzer. Infrared Laser Eng. 44(3), 897–900 (2015)
Sutili, T., Figueiredo, R.C., Conforti, E.: Laser linewidth and phase noise evaluation using heterodyne offline signal processing. J. Lightwave Technol. 34(21), 4933–4940 (2016)
Vázquez-Otero, A., Khikhlukha, D., Solano-Altamirano, J.M., et al.: Laser spot detection based on reaction diffusion. Sensors 16(3), 315–326 (2016)
Zhang, H.Z., Yao, M., Lei, P., et al.: Research of image processing method of far-field laser spots. Laser Technol. 4(37), 460–463 (2013)
Xiang, X.Y., Chen, H.Q., Peng, W.U., et al.: Adaptive measuring algorithm of laser beam width based on CCD. Laser Technol. 30(5), 552–554 (2006)
Wang, Y., Wang, Q., Ma, C., et al.: Factors affecting the accurate measurement of laser beam width with CCD camera. Chin. J. Lasers 41(2), 303–308 (2014)
Guo, J., Wei, Z., Miao, D.: Lane detection method based on improved RANSAC algorithm. In: 12th IEEE International Symposium on Autonomous Decentralized Systems (ISADS), pp. 285–288. IEEE Press, Taichung (2015)
Pollard, E., Gruyer, D., Tarel, J.P., et al.: Lane marking extraction with combination strategy and comparative evaluation on synthetic and camera images. In: 14th International IEEE Conference on Intelligent Transportation Systems Council (ITSC), pp. 1741–1746. IEEE Press, Washington (2011)
Jacobs, L., Weiss, J., Dolan, D.: Object tracking in noisy radar data: comparison of Hough transform and RANSAC. In: 2013 IEEE International Conference on Electro/Information Technology (EIT), pp. 1–6. IEEE Press, Rapid City (2013)
Kröger, M., Sauer-Greff, W., Urbansky, R., et al.: Performance evaluation on contour extraction using Hough transform and RANSAC for multi-sensor data fusion applications in industrial food inspection. In: 2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), pp. 234–237. IEEE Press, Poznan (2016)
Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th International Conference on Machine Learning (ICML), vol. 1, pp. 282–289. ACM, Williamstown (2001)
Tran, L.C., Pal, C.J., Nguyen, T.Q.: View synthesis based on conditional random fields and graph cuts. In: 17th IEEE International Conference on Image Processing (ICIP), pp. 433–436. IEEE Press, Hong Kong (2010)
Hur, J., Kang, S.N., Seo, S.W.: Multi-lane detection in urban driving environments using conditional random fields. In: 2013 IEEE Intelligent Vehicles Symposium (IV), pp. 1297–1302. IEEE Press, Gold Coast City (2013)
Qian, S., Chen, Z.H., Lin, M.Q., et al.: Saliency detection based on conditional random field and image segmentation. Acta Automatica Sinica 41(4), 711–724 (2015)
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Wang, X., Wang, H., Yang, A., Fei, M., Shen, C. (2017). Detection Method of Laser Level Line Based on Machine Vision. In: Fei, M., Ma, S., Li, X., Sun, X., Jia, L., Su, Z. (eds) Advanced Computational Methods in Life System Modeling and Simulation. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 761. Springer, Singapore. https://doi.org/10.1007/978-981-10-6370-1_48
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DOI: https://doi.org/10.1007/978-981-10-6370-1_48
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