Method for detecting surface waviness of metal sheet based on machine vision
-
摘要: 常规波纹度测量方法存在检测效率低、检测精度不高等问题,为了实现金属板材表面波纹度的快速准确检测,提出了一种非接触式的表面波纹度无损检测方法,该方法采用改进的中值滤波实现高效快速的图像去噪,使用霍夫变换实现图像的自动旋转,提取了表面图像基于灰度共生矩阵的纹理特征参数,并进行相关性分析。结果表明,对比度、能量、相关和熵等特征参数与波纹度之间存在较好的一致性。以对比度、能量、相关和熵等为输入参数,构建了基于BP神经网络的表面波纹度检测模型,试验验证该方法能够实现金属板材表面波纹度的高效、高精度测量,检测误差仅为4.90%。Abstract: The conventional waviness measurement method has the disadvantages of low efficiency and low measurement accuracy. In order to achieve accurate and rapid detection of the surface waviness of a metal sheet, a noncontact nondestructive method for surface waviness detection is proposed. This method uses improved median filtering to achieve efficient and fast image denoising, and in addition it uses Hough transform to realize the image. The automatic rotation of the image extraction method extracts the texture feature parameters of the surface image based on the gray level cooccurrence matrix and performs correlation analysis. The results show that there is a good agreement between the waviness and characteristic parameters of contrast, energy, correlation and entropy. Using contrast, energy, correlation and entropy as input parameters, a surface waviness detection model based on BP neural network was constructed. Experiments verify that the method can achieve high-efficiency and high-precision measurement of the surface waviness of metal plates with a detection error of only 4. 90%.
-
Keywords:
- sheet metal /
- surface waviness /
- machine vision /
- BP neural network
-
-
[1] 李增铮.砂带磨削与砂带磨床的推广应用[J]. 电工合金, 1996(3):21-26. [2] 徐迎军. 宽幅砂带磨削工艺在金属加工中的应用[J]. 金刚石与磨料磨具工程, 2017, 37(2):82-86. [3] 徐光月. 铣削加工表面微观几何形貌仿真及其应用研究[D]. 西安:西安理工大学, 2007. [4] 贾孝伟, 冯益华, 石鹏辉,等. 表面完整性及其测量方法的研究[J]. 齐鲁工业大学学报, 2016, 30(2):67-71. [5] HORN B K P. Understanding image intensities[J].Artificial Intelligence,1977,8(2):201-231.
[6] WOODHAM R J. Photometric method for determining surface orientation from multiple images[J]. Optical Engineering, 1980, 19(1):139-144.
[7] ZHANG X Z. Analysis of 3-D surface waviness on standard artifacts by retroreflective metrology[J]. Optical Engineering, 2000, 39(1):183.
[8] 敖鹏. 基于机器视觉的磨削表面波纹度检测方法研究[D]. 长沙:湖南大学, 2017. [9] 胡凤英. 波纹度参数的研究及检测[J]. 内燃机配件, 2006(2):38-39. [10] 侯远韶. 机器视觉系统中光源的选择[J]. 洛阳师范学院学报, 2014(8):45-49. [11] 孙晓昕, 曲伟, 侯力梅, 等. 空域滤波与频域滤波下数字图像平滑比较[J]. 黑龙江大学工程学报, 2014, 5(4):76-81. [12] 周冠玮, 平西建, 程娟. 基于改进Hough变换的文本图像倾斜校正方法[J]. 计算机应用, 2007, 27(7):1813-1816. [13] 王辉. 基于灰度共生矩阵木材表面纹理模式识别方法的研究[D]. 哈尔滨:东北林业大学, 2007. [14] 李昂. 基于改进BP算法的热轧带钢力学性能软测量[D]. 沈阳:东北大学, 2009. [15] 戚德虎, 康继昌. BP神经网络的设计[J]. 计算机工程与设计, 1998(2):47-49.
计量
- 文章访问数: 2
- HTML全文浏览量: 0
- PDF下载量: 2