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Flaw sizing using ultrasonic C-scan imaging with dynamic thresholds

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成果类型:
期刊论文
作者:
Li, Xiongbing;Wang, Yilin;Ni, Peijun;Hu, Hongwei*;Song, Yongfeng
通讯作者:
Hu, Hongwei
作者机构:
[Li, Xiongbing; Wang, Yilin; Song, Yongfeng] Cent S Univ, Sch Traff & Transportat Engn, Changsha 410075, Hunan, Peoples R China.
[Ni, Peijun] Ordnance Sci Inst China, Ningbo Branch, Ningbo 315103, Zhejiang, Peoples R China.
[Hu, Hongwei] Changsha Univ Sci & Technol, Coll Automot & Mech Engn, Changsha 410114, Hunan, Peoples R China.
通讯机构:
[Hu, Hongwei] C
Changsha Univ Sci & Technol, Coll Automot & Mech Engn, Changsha 410114, Hunan, Peoples R China.
语种:
英文
关键词:
Dynamic threshold;Flaw sizing;Generalised regression neural network;Image binarisation;Ultrasonic C-scan
期刊:
INSIGHT
ISSN:
1354-2575
年:
2017
卷:
59
期:
11
页码:
603-608
基金类别:
This work was supported by the National Natural Science Foundation of China (Grant nos 51575541 and 51711530231). The authors would like to express their gratitude to T D Ashworth for assistance with the English writing.
机构署名:
本校为通讯机构
院系归属:
汽车与机械工程学院
摘要:
The threshold for binarisation of an ultrasonic C-scan image has a significant effect on flaw sizing. The 6 dB drop method shows poor performance when extracting flaws from an ultrasonic image. A novel method is presented to improve the accuracy of flaw sizing, whereby the flaws are separated from the background using digital image processing techniques including the Otsu method, bilateral filtering and dilation. The optimised thresholds for each flaw are calculated using the enumeration method to binarise the original image and then the flaw size is predicted using the 6 dB drop method. The d...

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