马云鹏,李庆武,何飞佳,刘艳,席淑雅.金属表面缺陷自适应分割算法[J].仪器仪表学报,2017,38(1):245-251
金属表面缺陷自适应分割算法
Adaptive segmentation algorithm for metal surface defects
  
DOI:
中文关键词:  缺陷分割  灰度波动  邻域灰度差  PCA算法  误分错误率
英文关键词:defect segmentation  gray level fluctuation  neighborhood gray level difference  principal components analysis (PCA) algorithm  misclassification error rate
基金项目:国家自然科学基金(41306089)、江苏省产学研前瞻性联合研究项目(BY2014041)资助
作者单位
马云鹏 1.河海大学物联网工程学院常州213022;2.常州市传感网与环境感知重点实验室常州213022 
李庆武 1.河海大学物联网工程学院常州213022;2.常州市传感网与环境感知重点实验室常州213022 
何飞佳 1.河海大学物联网工程学院常州213022;2.常州市传感网与环境感知重点实验室常州213022 
刘艳 1.河海大学物联网工程学院常州213022;2.常州市传感网与环境感知重点实验室常州213022 
席淑雅 1.河海大学物联网工程学院常州213022;2.常州市传感网与环境感知重点实验室常州213022 
AuthorInstitution
Ma Yunpeng 1. College of Internet of Things Engineering, Hohai University, Changzhou 213022, China; 2. Changzhou Key Laboratory of Sensor Networks and Environmental Sensing ,Changzhou 213022, China 
Li Qingqu 1. College of Internet of Things Engineering, Hohai University, Changzhou 213022, China; 2. Changzhou Key Laboratory of Sensor Networks and Environmental Sensing ,Changzhou 213022, China 
He Feijia 1. College of Internet of Things Engineering, Hohai University, Changzhou 213022, China; 2. Changzhou Key Laboratory of Sensor Networks and Environmental Sensing ,Changzhou 213022, China 
Liu Yan 1. College of Internet of Things Engineering, Hohai University, Changzhou 213022, China; 2. Changzhou Key Laboratory of Sensor Networks and Environmental Sensing ,Changzhou 213022, China 
Xi Shuya 1. College of Internet of Things Engineering, Hohai University, Changzhou 213022, China; 2. Changzhou Key Laboratory of Sensor Networks and Environmental Sensing ,Changzhou 213022, China 
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中文摘要:
      金属表面缺陷的种类多、环境复杂度高,现有的金属表面缺陷分割算法有效性低、适用范围窄,为此提出一种金属表面缺陷自适应分割算法。该算法首先从8个方向对金属表面的灰度图像进行转换,根据多幅图像灰度波动状况,自适应地改变邻域灰度差分割算法中的阈值与步长对相应的图像进行分割,最后利用PCA算法将多幅图像压缩至单幅图像。实验结果表明,与现有的分割算法相比,该算法不仅适用于多种类型的金属表面缺陷部分的分割,而且分割准确度高。
英文摘要:
      In complex environment, there are many kinds of defects on the surface of metal. Existing metal surface defect segmentation algorithm has the deficiencies of low efficiency and narrow application scope, to solve these problems, an adaptive segmentation algorithm for metal surface defects is proposed in this paper. In this algorithm, firstly, the gray level images of the metal surfaces are transformed from eight directions. And then, according to the gray level fluctuation of the multiple images, the threshold and step length in neighborhood gray level difference segmentation algorithm are adaptively changed, and corresponding image in each direction is segmented. Lastly, all the processed images are compressed to a single image with PCA algorithm. Experiment results indicate that compared with existing segmentation algorithms, the proposed algorithm not only can be applied to segment various kinds of metal surface defects, but also has high segmentation accuracy.
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