红钢棒材表面缺陷图像采集与检测系统研究
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摘要
棒材是工业生产中重要的原材料,其表面质量也因此受到高度重视,在生产过程中,由于加工设备,工艺流程等产生的裂纹、刮伤、凹坑、耳子等缺陷会成为应力集中点、性能突变点的源头,影响产品的耐磨性、疲劳强度和硬度等。实现棒材表面缺陷在线检测并找出缺陷产生原因成为质量控制和工艺改进的关键环节,并且可为后续的质量等级划分提供数据支持,有效降低工人劳动强度,提高生产效率。本课题从机器视觉角度出发,对红钢表面缺陷在线检测系统进行研究,开展了如下工作：
设计了棒材表面缺陷视觉在线检测系统,通过对棒材表面进行图像采集和处理,判断其表面有无缺陷。首先介绍了检测棒材表面缺陷的机器视觉系统构成和工作原理,根据红钢棒材自身的特性选择相关的硬件设备。棒材加工线速度较快,表面图像的快速采集是实时在线检测红钢表面缺陷的先决条件。实际生产线上的高温棒材本身辐射红外光及可见光,影响到红钢表面图像的采集,使得采集的图像中缺陷信号不明显,因此对比分析多种光源特点,选择了强度高的激光线光源,并且设计了滤镜装置使得采集的红钢表面图像尽可能灰度均匀；根据检测要求及实际工况,选定了线阵CCD相机和长焦镜头作为图像采集单元；并确定了合理的布置方案,采用前向照明方式,激光线光源和线阵相机处在同一平面,同时两者与红钢轴线垂直,易于形成清晰的红钢表面图像。
研究了棒材表面图像的预处理算法。相机视场中的棒材表面是圆柱面,工业相机采集到的灰度图像中间亮度高,两侧较暗,并且图像两侧包含大量与缺陷检测无关的黑色背景信息,影响缺陷检测的准确性,重点研究了基于仿生学的遗传算法,采用二进制编码方案进行遗传操作,将求解得到的最优解用于图像分割,提取检测所需要的目标区域,剔除多余背景图像。遗传算法模拟生物种群的进化,搜索规模大,不易陷入局部最优,收敛速度快,自适应性强,可以有效的寻找参量空间的全局最优值,求得最优分割阈值。图像在采集、传输等过程中受到噪声信号干扰,降低红钢表面图像的质量,加大缺陷检测难度,为此,研究了基于局域统计特性滤波算法和基于二维Gabor变换的滤波算法,并对钢材表面图像进行了滤波实验和分析。Gabor变换通过小波的伸缩和平移对图像进行多尺度分析,具有信噪分离的良好性质,基于二维Gabor变换的滤波算法在滤波方面表现出色,对图像的边缘细节信息保留的效果良好。
研究了棒材表面缺陷的检测算法。首先介绍了常见的棒材表面缺陷和在图像中的表现特征,对实际棒材生产线上采集的红钢表面图像进行分析,统计了图像的灰度特征,针对凹痕缺陷,选取了近似红钢表面缺陷凹痕形状的正弦函数与像素灰度值做卷积运算,依据函数响应值的大小做出分析,检测是否存在凹痕缺陷。对于擦伤缺陷,结合小波分解算法对其进行检测,采用非抽样小波分解方法得到其各个方向的细节系数,将竖直细节系数与高斯函数作卷积,突出缺陷的边缘特征,求得其能量均值,应用高低阈值方法进行二值化处理,最后结合形态学理论得到完整的缺陷形状。
在线测试了棒材表面缺陷检测系统的现场应用情况。调试了图像采集软件部分的控制参数,对像素宽度、曝光时间、采集的频率进行了反复测试,以完成高质量的红钢表面图像采集工作。实际测试了硬件设备在生产现场的布置方案,线性激光光源必须与线阵相机处于同一竖直线上以配合图像采集工作,验证了前文从理论角度出发设计的整体布置方案的正确性。
The bar is an important raw material in industrial production, the quality of its surface has been highly valued. In the process of production, defects such as cracks, scratches, pits, ears which produced by the processing equipments and processes will become the sources of the stress concentration points and performance mutation points. These would influence the products'wear resistance, fatigue strength and hardness. Realization of the bar's online surface defects detection and finding out the causes become the key link of quality control and process improvement, besides, it can provide data support for the following quality rating, reducing the workers'labor intensity and improving the production efficiency. This topic study the online detection system of red steel's surface defect start from the machine view, carrying out the following works:
The online detection system of bar's surface detect was designed, that is check the surface quality through the surface images'acquisition and processing. First introduce the machine vision system's structure and working principle of surface defects' detection, and choose relevant hardware equipment according to the red-steel bar's own characteristics. The bar processing's line speed is relatively fast, surface images'rapid acquisition is the preliminary requirement of real-time online detection. The high temperature of actual production line exposes infrared light and visible light; this would influence the surface image's acquisition and weaken the defect signals of collected images. After comparing multiple light sources'characteristics, we choose high intensity laser line light source and design filter device to make the surface images'gray scale evenly as much as possible. According to testing requirements and actual conditions, we choose linear array CCD camera and a telephoto lens as the image acquisition unit. And determine the reasonable layout scheme to form clear red-steel surface image easily. That is adopting forward lighting, putting laser line light source and line array camera on the same plane, keeping these two perpendicular to red-steel's axes.
The image preprocessing algorithm of steel rod surface was studied. The steel rod surface in camera view is cylindrical. The gray level images captured by industrial camera are bright in middle and dark in two sides. Besides, the images contain much dark background information which has nothing to do with defect inspection. The genetic algorithm based on bionics was emphatically researched. The binary encode method was used to do genetic operation, and the optimal solution was used to segment images. The object area was extracted and the useless background information was deleted. Genetic algorithm simulates the evolution of population. The scale of search is big and it is difficult to enter local optimal solution. It has big speed to convergence and has strong adaptive ability. It can effectively find out the global optimal solution in the parameter space and search the optimal threshold for segmentation. The steel rod surface images are influenced in the capture and transmission parts, so the quality reduced. This enhances the difficulty of inspection. To solve this problem, the filter algorithm based on local statistical characteristic and two dimensions Gabor transformer was studied. The experiments were carried out and anglicized. Gabor transformation has good property for separating information and noise. The filter algorithm based on two dimensions Gabor transformation has good effect for keeping the edge information of images.
The inspection algorithm for steel rod surface defects was studied. The characteristics of common steel rod surface defects in image were introduced firstly. Through the analysis of red steel surface images captured in actual line, the gray lever characteristics of images were accounted. The sinusoidal function which seems like the pit shape on steel rod surface is selected to make convolution with the pixels gray values. According to the response value, we can judge whether the image has defect. For scratches defects, wavelet decomposition algorithm was used. The non-sampling wavelet decomposition method was used to get the coefficients in every direction. The vertical detail coefficients were used to make convolution with Gaussian function and the edge characteristic of defects was emphasized. The mean value of energy was computed and the high-low threshold method was employed to make image binary. At last, the whole shape of scratch defect was obtained through morphologic theory.
The applications of rod surface defects detection system was tested online. In order to accomplish the high-quality surface image capture of red steel, the control parameters of image capture software are debugged, such as the width of the pixel, the time of exposure and the acquisition frequency. The layout plan of hardware equipment in work site was tested. Linear laser light source must be in the same vertical line with the linear array camera for image acquisition. All of this validated the correctness of the whole layout plan from the point of theory.
设计了棒材表面缺陷视觉在线检测系统,通过对棒材表面进行图像采集和处理,判断其表面有无缺陷。首先介绍了检测棒材表面缺陷的机器视觉系统构成和工作原理,根据红钢棒材自身的特性选择相关的硬件设备。棒材加工线速度较快,表面图像的快速采集是实时在线检测红钢表面缺陷的先决条件。实际生产线上的高温棒材本身辐射红外光及可见光,影响到红钢表面图像的采集,使得采集的图像中缺陷信号不明显,因此对比分析多种光源特点,选择了强度高的激光线光源,并且设计了滤镜装置使得采集的红钢表面图像尽可能灰度均匀；根据检测要求及实际工况,选定了线阵CCD相机和长焦镜头作为图像采集单元；并确定了合理的布置方案,采用前向照明方式,激光线光源和线阵相机处在同一平面,同时两者与红钢轴线垂直,易于形成清晰的红钢表面图像。
研究了棒材表面图像的预处理算法。相机视场中的棒材表面是圆柱面,工业相机采集到的灰度图像中间亮度高,两侧较暗,并且图像两侧包含大量与缺陷检测无关的黑色背景信息,影响缺陷检测的准确性,重点研究了基于仿生学的遗传算法,采用二进制编码方案进行遗传操作,将求解得到的最优解用于图像分割,提取检测所需要的目标区域,剔除多余背景图像。遗传算法模拟生物种群的进化,搜索规模大,不易陷入局部最优,收敛速度快,自适应性强,可以有效的寻找参量空间的全局最优值,求得最优分割阈值。图像在采集、传输等过程中受到噪声信号干扰,降低红钢表面图像的质量,加大缺陷检测难度,为此,研究了基于局域统计特性滤波算法和基于二维Gabor变换的滤波算法,并对钢材表面图像进行了滤波实验和分析。Gabor变换通过小波的伸缩和平移对图像进行多尺度分析,具有信噪分离的良好性质,基于二维Gabor变换的滤波算法在滤波方面表现出色,对图像的边缘细节信息保留的效果良好。
研究了棒材表面缺陷的检测算法。首先介绍了常见的棒材表面缺陷和在图像中的表现特征,对实际棒材生产线上采集的红钢表面图像进行分析,统计了图像的灰度特征,针对凹痕缺陷,选取了近似红钢表面缺陷凹痕形状的正弦函数与像素灰度值做卷积运算,依据函数响应值的大小做出分析,检测是否存在凹痕缺陷。对于擦伤缺陷,结合小波分解算法对其进行检测,采用非抽样小波分解方法得到其各个方向的细节系数,将竖直细节系数与高斯函数作卷积,突出缺陷的边缘特征,求得其能量均值,应用高低阈值方法进行二值化处理,最后结合形态学理论得到完整的缺陷形状。
在线测试了棒材表面缺陷检测系统的现场应用情况。调试了图像采集软件部分的控制参数,对像素宽度、曝光时间、采集的频率进行了反复测试,以完成高质量的红钢表面图像采集工作。实际测试了硬件设备在生产现场的布置方案,线性激光光源必须与线阵相机处于同一竖直线上以配合图像采集工作,验证了前文从理论角度出发设计的整体布置方案的正确性。
The bar is an important raw material in industrial production, the quality of its surface has been highly valued. In the process of production, defects such as cracks, scratches, pits, ears which produced by the processing equipments and processes will become the sources of the stress concentration points and performance mutation points. These would influence the products'wear resistance, fatigue strength and hardness. Realization of the bar's online surface defects detection and finding out the causes become the key link of quality control and process improvement, besides, it can provide data support for the following quality rating, reducing the workers'labor intensity and improving the production efficiency. This topic study the online detection system of red steel's surface defect start from the machine view, carrying out the following works:
The online detection system of bar's surface detect was designed, that is check the surface quality through the surface images'acquisition and processing. First introduce the machine vision system's structure and working principle of surface defects' detection, and choose relevant hardware equipment according to the red-steel bar's own characteristics. The bar processing's line speed is relatively fast, surface images'rapid acquisition is the preliminary requirement of real-time online detection. The high temperature of actual production line exposes infrared light and visible light; this would influence the surface image's acquisition and weaken the defect signals of collected images. After comparing multiple light sources'characteristics, we choose high intensity laser line light source and design filter device to make the surface images'gray scale evenly as much as possible. According to testing requirements and actual conditions, we choose linear array CCD camera and a telephoto lens as the image acquisition unit. And determine the reasonable layout scheme to form clear red-steel surface image easily. That is adopting forward lighting, putting laser line light source and line array camera on the same plane, keeping these two perpendicular to red-steel's axes.
The image preprocessing algorithm of steel rod surface was studied. The steel rod surface in camera view is cylindrical. The gray level images captured by industrial camera are bright in middle and dark in two sides. Besides, the images contain much dark background information which has nothing to do with defect inspection. The genetic algorithm based on bionics was emphatically researched. The binary encode method was used to do genetic operation, and the optimal solution was used to segment images. The object area was extracted and the useless background information was deleted. Genetic algorithm simulates the evolution of population. The scale of search is big and it is difficult to enter local optimal solution. It has big speed to convergence and has strong adaptive ability. It can effectively find out the global optimal solution in the parameter space and search the optimal threshold for segmentation. The steel rod surface images are influenced in the capture and transmission parts, so the quality reduced. This enhances the difficulty of inspection. To solve this problem, the filter algorithm based on local statistical characteristic and two dimensions Gabor transformer was studied. The experiments were carried out and anglicized. Gabor transformation has good property for separating information and noise. The filter algorithm based on two dimensions Gabor transformation has good effect for keeping the edge information of images.
The inspection algorithm for steel rod surface defects was studied. The characteristics of common steel rod surface defects in image were introduced firstly. Through the analysis of red steel surface images captured in actual line, the gray lever characteristics of images were accounted. The sinusoidal function which seems like the pit shape on steel rod surface is selected to make convolution with the pixels gray values. According to the response value, we can judge whether the image has defect. For scratches defects, wavelet decomposition algorithm was used. The non-sampling wavelet decomposition method was used to get the coefficients in every direction. The vertical detail coefficients were used to make convolution with Gaussian function and the edge characteristic of defects was emphasized. The mean value of energy was computed and the high-low threshold method was employed to make image binary. At last, the whole shape of scratch defect was obtained through morphologic theory.
The applications of rod surface defects detection system was tested online. In order to accomplish the high-quality surface image capture of red steel, the control parameters of image capture software are debugged, such as the width of the pixel, the time of exposure and the acquisition frequency. The layout plan of hardware equipment in work site was tested. Linear laser light source must be in the same vertical line with the linear array camera for image acquisition. All of this validated the correctness of the whole layout plan from the point of theory.
引文
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