Toward intelligent manufacturing: label characters marking and recognition method for steel products with machine vision
Qi-Jie Zhao, Peng Cao, Da-Wei Tu
Advances in Manufacturing ›› 2014, Vol. 2 ›› Issue (1) : 3-12.
Toward intelligent manufacturing: label characters marking and recognition method for steel products with machine vision
Correctly coding materials and identifying characters marked on materials are very important for steel manufacturing industry to realize informatization management and intelligent manufacturing. However, the steel products manufacturing is often in a high temperature environment, and there are a lot of material storage and retrieval processes, workers are not easily close to the environment and complete tasks, so it is a great challenge to automatically mark and identify characters on the steel products. This paper presents a framework of label characters marking and management for steel materials, furthermore, a kind of marked characters online detection and tracking method has been provided based on machine vision. In addition, some experiments have been done in BaoSteel to mark characters on hot billets and recognize them in multi situations, and the results show that the proposed method is practical, and has provided a helpful exploration in obtaining accurate fundamental data for the intelligent manufacturing system in steelworks.
Intelligent manufacturing / Enterprises informatization / Label character marking / Machine vision / Marked character recognition
[1.] |
|
[2.] |
Iida O, Urakami M, Iwamura T (1993) Applications and evaluation of AI technology in the steel industry. In: IEEE 2nd international workshop on emerging technologies and factory automation, New York, pp 156–163
|
[3.] |
|
[4.] |
Wang X, Yu S, Zheng B et al (2006) Intelligent scheduling system of steelmaking and continuous casting based on ERP/MES/PCS. In: The sixth world congress on intelligent control and automation, IEEE, New York, pp 7381–7384
|
[5.] |
|
[6.] |
Agarwal K, Shivpuri R (2013) Knowledge discovery in steel bar rolling mills using scheduling data and automated inspection. J Intell Manuf. doi:10.1007/s10845-013-0730-5 (online)
|
[7.] |
Kang D, Park C, Won S (2008) Robust image binarization method for billet identification in steelmaking process. In: IECON 2008, 34th annual conference of IEEE, industrial electronics. IEEE, New York, pp 1539–1544
|
[8.] |
Choi SH, Yun JP, Sim SB et al (2010) Edge-based text localization and character segmentation algorithms for automatic slab information recognition. In: 2010 International conference on image analysis and signal processing (IASP). IEEE, New York, pp 387–392
|
[9.] |
Bulnes FG, Usamentiaga R, Garcia DF et al (2011) Vision-based technique for periodical defect detection in hot steel strips. In: Industry applications society annual meeting (IAS). IEEE, New York, pp 1–8
|
[10.] |
Jia H, Murphey Y L, Shi J et al (2004) An intelligent real-time vision system for surface defect detection. In: ICPR 2004, Proceedings of the 17th international conference on pattern recognition, 2004. IEEE, New York, pp 239–242
|
[11.] |
|
[12.] |
Park S, Lee J (2006) Development of real-time character recognition system for the steel-iron plant. In: ICHIT’06, International conference on hybrid information technology, 2006. IEEE, New York, pp 287–292
|
[13.] |
Yanagita T, Tanaka T, Toshima A (1995) A neural network recognition system for machine printed characters on coils. In: The thirtieth IAS annual meeting, IAS’95, conference record of the 1995 IEEE. IEEE, New York, pp 1795–1799
|
[14.] |
Park C, Won S (2007) Development of recognition system for billet identification. In: SICE, 2007 annual conference. IEEE, New York, pp 68–71
|
[15.] |
Shim SB, Choi SH, Kim SW (2010) Text localization using valid optical flow for recognition of slab numbers. In: 2010 International conference on control automation and systems (ICCAS). IEEE, New York, pp 324–329
|
[16.] |
|
[17.] |
|
[18.] |
|
[19.] |
|
[20.] |
Dubey P, Sinthupinyo W (2010) New approach on structural feature extraction for character recognition. In: 2010 International symposium on communications and information technologies (ISCIT). IEEE, New York, pp 946–949
|
[21.] |
|
[22.] |
Rajkumar J, Mariraja K, Kanakapriya K et al (2012) Two schemas for online character recognition of Telugu script based on support vector machines. In: 2012 International conference on frontiers in handwriting recognition (ICFHR). IEEE, New York, pp 565–570
|
[23.] |
|
[24.] |
Wei X (2011) Research of digital character recognition technology based on BP algorithm. In: Advances in Computer Science, Environment, Ecoinformatics, and Education. Springer, Berlin, pp 551–555
|
[25.] |
|
/
〈 |
|
〉 |