刘琴琴,周慧云,王兴洲.基于灰度均衡模型联合Gabor滤波器的钢轨表面缺陷检测方法[J].表面技术,2018,47(11):290-294.
LIU Qin-qin,ZHOU Hui-yun,WANG Xing-zhou.Research on Rail Surface Defect Detection Method Based on Gray Equalization Model Combined with Gabor Filter[J].Surface Technology,2018,47(11):290-294 |
| 基于灰度均衡模型联合Gabor滤波器的钢轨表面缺陷检测方法 |
| Research on Rail Surface Defect Detection Method Based on Gray Equalization Model Combined with Gabor Filter |
| 投稿时间：2018-09-01 修订日期：2018-11-20 |
| DOI：10.16490/j.cnki.issn.1001-3660.2018.11.041 |
| 中文关键词: 钢轨表面缺陷 灰度均衡模型 相位谱 谱残差 ostu阈值分割 Gabor滤波器 |
| 英文关键词:detection of rail surface defect gray scale equilibrium model phase spectrum spectral residual Ostu threshold segmentation Gabor filter |
| 基金项目:南通理工学院科技创新与服务地方团队；江苏省高校自然科学基金项目（16KJB520039）；江苏省现代教育技术研究课题（2017-R-54624） |
|
|
|
|
| 摘要点击次数: |
| 全文下载次数: |
| 中文摘要: |
| 目的 设计一种钢轨表面缺陷检测方法，对钢轨表面存在的缺陷进行快速、准确地检测。方法 首先，利用图像的灰度均值构造灰度均衡模型，对钢轨表面图像中像素点的灰度值进行修正，以克服光照不均的影响。然后，利用图像的谱残差模型与相位谱增强钢轨表面图像中的缺陷部分，引入ostu阈值分割法对增强后的图像进行二值化，对图像中的缺陷区域进行准确地分割提取。最后，利用Gabor滤波器，将二值化图像中的噪声进行滤除，并保留缺陷区域的边缘等细节特征。结果 与对照组方法相比，所提方法的检测效果较好，精确率以及召回率都有所提高。直观测试结果显示，所提方法能够较为完整地检测出钢轨表面缺陷。客观测试实验结果显示，所提方法的精确率为90.11%，召回率为93.41%，且平均耗时为45.17 ms，相对对照组方法而言，耗时最少。结论 所提钢轨表面缺陷方法不仅能够准确地对钢轨表面缺陷进行检测，而且还具有较高的检测效率。 |
| 英文摘要: |
| The work aims to design a detection method to detect the defects on the rail surface quickly and accurately. Firstly, the gray-level equalization model was constructed by the gray-level mean of the image, and the gray-level value of the pixels in the rail surface image was corrected to overcome the influence of uneven illumination. Then, the spectral residual model of the image and phase spectrum were used to enhance the defects in the rail surface image. Secondly, threshold segmentation method was introduced to binarize the enhanced image, and the defective areas in the image were segmented and extracted accurately. Finally, the noise in the binary image was filtered by the filter, and the edge of the defect area and other details were preserved. Compared with the control group, the proposed method had better detection effect and higher precision and recall rate. The intuitive test results showed that the method proposed in this paper could detect the surface defects of the rail more completely. From the objective test results, the accuracy rate of the proposed method was 90.11% and the recall rate was 93.41%. At the same time, the objective test results also showed that the average time-consuming method was 45.17 ms. Compared with the control group method, the time-consuming was the least. Therefore, the method proposed in this paper could detect rail surface defects more accurately and rapidly. Therefore, the rail surface defect method proposed in this paper can not only accurately detect the rail surface defect, but also has high detection efficiency. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| 关闭 |
渝公网安备 50010702501715号