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Steel bars counting and splitting method based on machine vision
Wu Y(吴阳); Zhou XF(周晓锋); Zhang YC(张宜弛)
Department数字工厂研究室
Conference Name2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
Conference DateJune 8-12, 2015
Conference PlaceShenyang, China
Source Publication2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
PublisherIEEE
Publication PlacePiscataway, NJ, USA
2015
Pages420-425
Indexed ByEI ; CPCI(ISTP)
EI Accession number20161402187573
WOS IDWOS:000380502300081
Contribution Rank2
ISSN2379-7711
ISBN978-1-4799-8730-6
KeywordSteel Bars Count Concave Point Segment Match Klevel Fault Tolerance Visual Feedback
AbstractThis paper proposes a novel on-line steel bars counting and splitting method based on machine vision which uses concave dots matching to segment, K-level fault tolerance to count and visual feedback to multiple split automatically. Firstly, it preprocesses images of bars and uses connected area analysis to obtain edge profile of adherent bars, then scans concave areas in the contour and find concave dots. Secondly, it uses concave dot matching condition to segment and counts single bar after segmentation to achieve counting purpose through movement estimation and K-level fault tolerance algorithm. Finally, visual feedback is presented, if preliminary split is wrong, redraw the line for splitting and drive the splitting mechanism again. Experiment results show that the method has a high accuracy for segmentation of adherent bars, and can split steel bars accurately. The accuracy ratio of segmentation for steel bars whose diameters are between 8mm and 20mm is more than 99.90%, which satisfies the accepted standard of enterprises.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/18527
Collection数字工厂研究室
Affiliation1.Wuxi CAS Ubiquitous Information Technology RandD, Center Co., Ltd, Wuxi, China
2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
Recommended Citation
GB/T 7714
Wu Y,Zhou XF,Zhang YC. Steel bars counting and splitting method based on machine vision[C]. Piscataway, NJ, USA:IEEE,2015:420-425.
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