基于纹理特征的钢轨表面缺陷检测
Rail Surface Defect Detection Based on Textural Features
李刚 1丁运峰 2张宇豪3
作者信息
- 1. 安徽城市管理职业学院,安徽 合肥 230011
- 2. 芜湖固高自动化技术有限公司,安徽 芜湖 241060
- 3. 南京铁道职业技术学院,江苏省 南京市 210031
- 折叠
摘要
为提高钢轨表面缺陷识别的准确率,采用双边滤波的方法去除噪声,较好地保留缺陷边界;优化阙值计算方法,结合灰度直方图峰值的个数,采用相应的阙值计算方法,确保二值化结果可靠、有效;应用频域滤波优化,消除低频、细微的干扰区域;通过不变矩特征提取,有效地区分掉块和压溃缺陷,从而提高了钢轨表面缺陷识别的智能化程度和准确率.
Abstract
In order to improve the accuracy of rail surface defect identification,the method of bilateral filtering is used to remove noise and preserve the defect boundary.The threshold calculation method is optimized,combined with the number of gray histogram peaks,adopts the corresponding threshold calculation method to ensure that the binarization results are reliable and effective.Frequency domain filter optimization is applied to eliminate low fre-quency and subtle interference areas.Through the invariant moment feature extraction,the falling block and col-lapse defects can be distinguished effectively,which improves the intelligence level and accuracy of rail surface defect identification.
关键词
Halcon/不变矩/钢轨/表面缺陷Key words
Halcon/invariant moment/rail/surface defect引用本文复制引用
基金项目
芜湖市科技项目重点研发项目(022yf25)
出版年
2024
NETL
NSTL
万方数据