热轧圆钢表面缺陷视觉在线检测算法研究
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摘要
热轧圆钢表面缺陷检测技术是提高企业产品竞争力、改进生产工艺的关键技术之一,而传统的表面缺陷无损检测技术难以适应高速热轧圆钢生产线需求,为了能够实时在线检测表面缺陷,基于机器视觉的表面缺陷检测技术应运而生,该技术检测速度快、准确率高,而且能够重现产品表面质量情况,因此很多公司企业投入巨资对其进行研究。目前,基于机器视觉的热轧圆钢表面缺陷检测技术在欧美发展的较为成熟,并且已有相关检测系统投入运行,而国内在这方面的研究刚处于起步阶段,与国外差距较大,这在一定程度上影响了我国热轧圆钢产品的市场竞争力,因而此项技术急需发展研究。
首先,开展硬件系统研究。设计了热轧圆钢表面缺陷检测系统总体方案,对相机的个数选择进行了分析,设计了光照系统；根据纵向分辨率检测要求选择了相机的类型和具体型号,根据横向分辨率要求确定了镜头的焦距,并选择出合适的镜头型号,比较了不同光源的特点,选择了适合本课题的光源类型,通过景深的计算验证了所选择硬件的正确性；介绍了图像采集装置,并进行了图像采集实验,分析了各个参数对图像采集的影响,列举了不同类型的圆钢表面图像；分析了热轧圆钢表面图像成像结果,总结了影响圆钢表面成像的三个因素；最后对采集的圆钢表面原始图像特征进行了定性和定量分析。
其次,提出了改进的局部边界搜索算法用于进行圆钢图像的提取,去除了采集的原始图像中存在的无用背景信息,仅保留了圆钢图像信息,减少了图像处理数据,避免了圆钢边界被误检为缺陷的情况；分析了圆钢表面图像中存在的噪声类型,建立了图像退化模型和噪声模型,得出图像中存在的噪声主要为高斯噪声；比较了不同滤波算法对圆钢表面图像的降噪效果,得出最适合本课题的滤波方法；利用理想低通滤波器进行噪声滤除,比较了矩形滤波器和圆形滤波器的降噪效果,最终确定了矩形滤波器滤波算法。
然后,分析了凹坑缺陷在图像中的表现特征,得出利用列像素检测凹坑缺陷更为有效；提出基于三角函数和韦伯对比度的凹坑检测改进算法,讨论了图像灰度值的修正方法、正弦核函数周期的选择以及阈值的选取问题,得到了较好的检测效果,但是算法受凹坑缺陷尺寸大小限制；提出了基于下包络韦伯对比度的凹坑缺陷检测算法,介绍了韦伯定律及其在视觉中的应用,引入了下包络、韦伯对比度和下包络韦伯对比度的概念,然后详细阐述了具体的检测算法,仿真实验结果表明该算法对于热轧圆钢表面凹坑缺陷具有非常高的检出率并且不受缺陷尺寸大小的影响。
提出基于局部环形对比度的热轧圆钢表面缺陷实时检测算法,该算法可以检测热轧圆钢表面产生的凹坑、刮伤和耳子等常见缺陷,并且具有较高的检出率和低误检率。首先分析这些缺陷在图像中表现出的共同特征,即缺陷所在处与局部背景图像之间存在较大的灰度对比度,这是该算法的检测依据,然后引入了局部环形背景和局部环形对比度的概念,并且利用已有的图像数据得出检测阈值与局部环形背景灰度均值之间的关系,使得阈值具有自适应性,检测结果更为准确,最后详细介绍了算法的具体实施过程,并且进行实验仿真,实时性测试实验表明该算法能够保证热轧圆钢表面缺陷的在线检测。
最后,为了测试所研究检测算法在真实热轧圆钢现场的应用效果,对前面提出的缺陷检测算法进行了编程实现,嵌入到线阵相机里检验效果。介绍了软件系统的整体框架和程序界面,分析了进行相机二次开发所做的主要内容；为了验证算法的有效性,即嵌入到相机的检测算法实时检测效果是否与实验室仿真结果一致,首先在车间磨床上进行了离线测试,即将一段圆钢成品放置于磨床上,使其来回纵向运动模拟圆钢轧制时的情形,测试结果表明二次开发后的线阵相机检测结果与算法在实验室的仿真结果一致,因此算法可行,并且讨论了不同光强对于图像采集质量的影响；然后将该系统应用于热轧圆钢现场进行在线测试,结果表明所研制的表面缺陷检测系统可以有效的检出圆钢轧制过程中产生的常见缺陷,并且实时性较好,可以进行工业化应用。
The detection technology for hot rolled steel bar surface defects is one of the key technologies to increase product competence and improve production process. However, traditional non-destructive testing technology for surface defects is hard to satisfy the requirement of high speed detection for hot steel bar. In order to detect surface defects on-line, the detection technology of surface defects based on machine vision appeared. This technology has high speed detection and accuracy. Besides, it can reappear the situation of production surface quality. So many companies make a huge investment to research this technology. So far, the detection technology for hot rolled steel bar surface defects based on machine vision is well-established in developed countries, and they have had relative detection system being in use. However, the research in this field of our country just started and has large gaps with developed country. This affects the market competitiveness of our country's hot steel bar product to some degree. So the develop and research of this technology is in urgent need.
Firstly, the hardware system was researched. The whole layout scheme for steel bar surface defects detection was designed. The selection of camera number was analysized. Then the light system was designed. According to the longitudinal resolution requirement, the type and specific model of camera was selected. The focus of lens was calculated according to the lateral resolution and the specific model was selected. Afterwards, we compared the characteristics of different light sources and chose appropriate light source type. The calculation of depth of field verified that the selected hardwares were correct. Then the imaging equipment was introduced and imaging experiment was carried out. The parameters'effect to imaging was analized. Then different types of steel bar surface images were listed. Besides, the imaging result of hot rolled steel bar surface images was analyzed. We concluded three factors of affecting the imaging of steel bar surface. Lastly, the characteristics of steel bar surface images were analized qualitatively and quantitatively.
To extract steel bar surface image, we proposed modified local border search algorithm. This removes useless background information existed in original images and retains steel bar information. It reduces image processing data and avoids the steel bar border being considered to defects. We analyzed the type of noise in steel bar surface images and built image degradation model. The noise in images is mainly gauss noise. The denoise effect of different filter algorithm was compared, and we obtained the most appropriate method for this project. The ideal low pass filter was used to denoise. Through the denoise effect comparision of rectangle filter and circular filter, we concluded that the rectangle filter was the best.
Afterwards, the characteristic of pits in images is analyzed and we obtained that it's more effective to use column pixels of images. We proposed a detection algorithm for pit defects based on trigonometric function and Weber contrast. Then the modified method for gray level of image, the cycle selection of sine kernel function and threshold selection were discussed. The detection result is good, but the algorithm is limited to the size of pit defects, so a pits defects detection algorithm based on lower envelope Weber contrast (LEWC) was proposed. Then the Weber's law and its application were introduced. Besides, we led into the concept of lower envelope Weber contrast and introduced detailed detection algorithm. The simulation experiments indicated that this algorithm is very effective to detect surface pits defects of hot rolled steel bar and it is not affected by the size of defects.
A hot rolled steel bar surface defects detection algorithm based on local annular contrast (LAC) was proposed. This algorithm can detect some common defects occurred on hot rolled steel bar. such as pits, scratches and overfills. Firstly, the common characteristic of these defects was analyzed. That is there is big gray level contrast between the defects and local background image. This is the foundation of the algorithm. Then the concepts of local annular background and local annular contrast were introduced, and the relationship between detection threshold and gray mean value of local annular background was obtained. This make the threshold adaptable and the detection result more accurate. Finally, the detailed algorithm process was introduced and we did experiment simulation. The real time test indicated that the algorithm can guarantee the on-line surface defects detection for hot rolled steel bar.
At last, In order to test the effect of the proposed algorithm in actual hot rolling line, we programmed the defects detection algorithm and put it into the linear camera. The overall frame and interface of the software system were introduced. The main contents for the camera re-development were analized. To verify the effect of the algorithm, we did off-line test on grinder to judge whether the detection results with the algorithm in camera and experiment simulation are the same. One piece of steel bar product was put on grinder and made do cyclic ground motion to simulate the steel bar rolled on hot rolling line. The result indicated that the effect of the algorithm in camera was the same with the experiment simulation, so the algorithm is workable. The effect of different light strength to the quality of images was discussed. Then the detection system was put on hot rolling line to make on-line test. The outcome demonstrated that the surface defects detection system could detect the usual defects produced on steel bar. Besides, the reali-time was good, and it can be employed in industrial application.
首先,开展硬件系统研究。设计了热轧圆钢表面缺陷检测系统总体方案,对相机的个数选择进行了分析,设计了光照系统；根据纵向分辨率检测要求选择了相机的类型和具体型号,根据横向分辨率要求确定了镜头的焦距,并选择出合适的镜头型号,比较了不同光源的特点,选择了适合本课题的光源类型,通过景深的计算验证了所选择硬件的正确性；介绍了图像采集装置,并进行了图像采集实验,分析了各个参数对图像采集的影响,列举了不同类型的圆钢表面图像；分析了热轧圆钢表面图像成像结果,总结了影响圆钢表面成像的三个因素；最后对采集的圆钢表面原始图像特征进行了定性和定量分析。
其次,提出了改进的局部边界搜索算法用于进行圆钢图像的提取,去除了采集的原始图像中存在的无用背景信息,仅保留了圆钢图像信息,减少了图像处理数据,避免了圆钢边界被误检为缺陷的情况；分析了圆钢表面图像中存在的噪声类型,建立了图像退化模型和噪声模型,得出图像中存在的噪声主要为高斯噪声；比较了不同滤波算法对圆钢表面图像的降噪效果,得出最适合本课题的滤波方法；利用理想低通滤波器进行噪声滤除,比较了矩形滤波器和圆形滤波器的降噪效果,最终确定了矩形滤波器滤波算法。
然后,分析了凹坑缺陷在图像中的表现特征,得出利用列像素检测凹坑缺陷更为有效；提出基于三角函数和韦伯对比度的凹坑检测改进算法,讨论了图像灰度值的修正方法、正弦核函数周期的选择以及阈值的选取问题,得到了较好的检测效果,但是算法受凹坑缺陷尺寸大小限制；提出了基于下包络韦伯对比度的凹坑缺陷检测算法,介绍了韦伯定律及其在视觉中的应用,引入了下包络、韦伯对比度和下包络韦伯对比度的概念,然后详细阐述了具体的检测算法,仿真实验结果表明该算法对于热轧圆钢表面凹坑缺陷具有非常高的检出率并且不受缺陷尺寸大小的影响。
提出基于局部环形对比度的热轧圆钢表面缺陷实时检测算法,该算法可以检测热轧圆钢表面产生的凹坑、刮伤和耳子等常见缺陷,并且具有较高的检出率和低误检率。首先分析这些缺陷在图像中表现出的共同特征,即缺陷所在处与局部背景图像之间存在较大的灰度对比度,这是该算法的检测依据,然后引入了局部环形背景和局部环形对比度的概念,并且利用已有的图像数据得出检测阈值与局部环形背景灰度均值之间的关系,使得阈值具有自适应性,检测结果更为准确,最后详细介绍了算法的具体实施过程,并且进行实验仿真,实时性测试实验表明该算法能够保证热轧圆钢表面缺陷的在线检测。
最后,为了测试所研究检测算法在真实热轧圆钢现场的应用效果,对前面提出的缺陷检测算法进行了编程实现,嵌入到线阵相机里检验效果。介绍了软件系统的整体框架和程序界面,分析了进行相机二次开发所做的主要内容；为了验证算法的有效性,即嵌入到相机的检测算法实时检测效果是否与实验室仿真结果一致,首先在车间磨床上进行了离线测试,即将一段圆钢成品放置于磨床上,使其来回纵向运动模拟圆钢轧制时的情形,测试结果表明二次开发后的线阵相机检测结果与算法在实验室的仿真结果一致,因此算法可行,并且讨论了不同光强对于图像采集质量的影响；然后将该系统应用于热轧圆钢现场进行在线测试,结果表明所研制的表面缺陷检测系统可以有效的检出圆钢轧制过程中产生的常见缺陷,并且实时性较好,可以进行工业化应用。
The detection technology for hot rolled steel bar surface defects is one of the key technologies to increase product competence and improve production process. However, traditional non-destructive testing technology for surface defects is hard to satisfy the requirement of high speed detection for hot steel bar. In order to detect surface defects on-line, the detection technology of surface defects based on machine vision appeared. This technology has high speed detection and accuracy. Besides, it can reappear the situation of production surface quality. So many companies make a huge investment to research this technology. So far, the detection technology for hot rolled steel bar surface defects based on machine vision is well-established in developed countries, and they have had relative detection system being in use. However, the research in this field of our country just started and has large gaps with developed country. This affects the market competitiveness of our country's hot steel bar product to some degree. So the develop and research of this technology is in urgent need.
Firstly, the hardware system was researched. The whole layout scheme for steel bar surface defects detection was designed. The selection of camera number was analysized. Then the light system was designed. According to the longitudinal resolution requirement, the type and specific model of camera was selected. The focus of lens was calculated according to the lateral resolution and the specific model was selected. Afterwards, we compared the characteristics of different light sources and chose appropriate light source type. The calculation of depth of field verified that the selected hardwares were correct. Then the imaging equipment was introduced and imaging experiment was carried out. The parameters'effect to imaging was analized. Then different types of steel bar surface images were listed. Besides, the imaging result of hot rolled steel bar surface images was analyzed. We concluded three factors of affecting the imaging of steel bar surface. Lastly, the characteristics of steel bar surface images were analized qualitatively and quantitatively.
To extract steel bar surface image, we proposed modified local border search algorithm. This removes useless background information existed in original images and retains steel bar information. It reduces image processing data and avoids the steel bar border being considered to defects. We analyzed the type of noise in steel bar surface images and built image degradation model. The noise in images is mainly gauss noise. The denoise effect of different filter algorithm was compared, and we obtained the most appropriate method for this project. The ideal low pass filter was used to denoise. Through the denoise effect comparision of rectangle filter and circular filter, we concluded that the rectangle filter was the best.
Afterwards, the characteristic of pits in images is analyzed and we obtained that it's more effective to use column pixels of images. We proposed a detection algorithm for pit defects based on trigonometric function and Weber contrast. Then the modified method for gray level of image, the cycle selection of sine kernel function and threshold selection were discussed. The detection result is good, but the algorithm is limited to the size of pit defects, so a pits defects detection algorithm based on lower envelope Weber contrast (LEWC) was proposed. Then the Weber's law and its application were introduced. Besides, we led into the concept of lower envelope Weber contrast and introduced detailed detection algorithm. The simulation experiments indicated that this algorithm is very effective to detect surface pits defects of hot rolled steel bar and it is not affected by the size of defects.
A hot rolled steel bar surface defects detection algorithm based on local annular contrast (LAC) was proposed. This algorithm can detect some common defects occurred on hot rolled steel bar. such as pits, scratches and overfills. Firstly, the common characteristic of these defects was analyzed. That is there is big gray level contrast between the defects and local background image. This is the foundation of the algorithm. Then the concepts of local annular background and local annular contrast were introduced, and the relationship between detection threshold and gray mean value of local annular background was obtained. This make the threshold adaptable and the detection result more accurate. Finally, the detailed algorithm process was introduced and we did experiment simulation. The real time test indicated that the algorithm can guarantee the on-line surface defects detection for hot rolled steel bar.
At last, In order to test the effect of the proposed algorithm in actual hot rolling line, we programmed the defects detection algorithm and put it into the linear camera. The overall frame and interface of the software system were introduced. The main contents for the camera re-development were analized. To verify the effect of the algorithm, we did off-line test on grinder to judge whether the detection results with the algorithm in camera and experiment simulation are the same. One piece of steel bar product was put on grinder and made do cyclic ground motion to simulate the steel bar rolled on hot rolling line. The result indicated that the effect of the algorithm in camera was the same with the experiment simulation, so the algorithm is workable. The effect of different light strength to the quality of images was discussed. Then the detection system was put on hot rolling line to make on-line test. The outcome demonstrated that the surface defects detection system could detect the usual defects produced on steel bar. Besides, the reali-time was good, and it can be employed in industrial application.
引文
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