CN112614088A - 基于3d视觉检测技术的识别与检测方法 - Google Patents
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Abstract
本发明公开了基于3D视觉检测技术的识别与检测方法,属于3D视觉检测技术领域,管道表面存在大量微小尺寸和微小形变缺陷,传统二维检测方法由于成像条件限制,对于这两类微小缺陷的检出率较低。本发明将立体三维测量方法应用于管道表面缺陷的三维在线检测,根据管道生产环境,在转辊一侧设置了线扫描方式的立体三维测量系统,采用入射角度±18°的红、绿单色光源在中间场范围进行照明,获得了对微小缺陷的良好成像效果。
Description
技术领域
本发明涉及3D视觉检测技术领域,尤其涉及基于3D视觉检测技术的识别与检测方法。
背景技术
机器视觉检测技术,以机器视觉方法作为理论基础,并综合运用了电子学、传感器技术、精密测量以及图像处理等相关技术,是诸如制造业、医疗诊断、军事等领域中各种智能系统不可分割的非接触检测方法。该检测技术的基本原理是对视觉系统得到的被测目标图像进行分析,并将得到的分析结果与已有的先验知识进行多方面的对比,最后判断被测目标是否符合规范凹。随着工业发展对智能化需求的逐步提高,该技术逐渐被引入到工业检测中,实现对物体(产品或零件)特征或位置等信息的测量,具有非接触、速度快、柔性好等突出优点,在智能制造业中有着广阔的应用前景,并逐渐成为智能制造业的基础技术之一;
目前市场上采用2D视觉检测方法受外部光照和颜色变化影响较大,测量精度易受照明条件影响;单台2D相机无法获取待检测的空间坐标信息,需要多台相机配合,成本较高,结构复杂。
发明内容
本发明为解决上述问题,而提出的基于3D视觉检测技术的识别与检测方法。
为了实现上述目的,本发明采用了如下技术方案:
基于3D视觉检测技术的识别与检测方法,包括以下步骤:
S1、采用经过标定的对称的线性光源和线阵相机结合的线扫描三维测量系统进行扫描;
式中,e为光源功率系数,通过标定步骤可获得;
S4、将三维深度图沿管道旋转方向,分隔为宽度为d的纵条,计算纵条内曲面投影ZPR如下:
采用采用d/2步长分割纵条区域进行曲面投影,曲面投影ZPR最终拼接成与三维深度图等高度的投影图像;
在区域C内计算局部标准差QSD和局部绝对差QRA,计算公式如下:
S5、将S4中经曲面投影ZPR、局部标准差QSD和局部绝对差QRA的通道特征图,经过均值滤波后,采用自适应阈值可以进行初步的缺陷区域分割。考虑到分割区域的连续性,采用形态学闭运算闭合连通区域,最终实现缺陷的准确检出和定位。
优选地,所述S1中的对称的线性光源采用红、绿色线形LED光源构成,绿光波长为555-585nm,红光波长620-639nm,光源照明范围具有一定的重叠区域,入射角度为±18°。
优选地,所述S3中的构建三维深度图包括以下步骤:
A1、通过S1中的线扫描三维测量系统,获得三维表面的x、y方向的梯度测量矩阵P、Q,通过积分计算三维曲面公式为:
式中,r为积分路径;
A2、将三维重建问题看做对一个曲面的拟合,建立关于梯度、图像数据的泛函损失函数,公式如下:
式中,误差项J1是光度立体测量范围Ω内,梯度测量值p、q与梯度计Zx、Zy间的误差;J2为正则化项;J3为约束表面法向量的连续性,抑制了“折痕”类型的梯度突变特征;正则化系数λ、μ取值为0;
A3、从x和y方向的梯度矩阵P和Q重建三维深度矩阵Z,并对A2中的公式进行优化,得到:
式中包含了对方向梯度q的优化,同时含有曲面梯度的正则化项;
A4、采用WO算法构建三维深度图。
优选地,所述S4中的d为32像素。
优选地,所述S4中的区域C为8×6像素的矩形区域
优选地,所述A4采用WO算法构建三维深度图中加入迭代优化步骤(GO),其迭代优化步骤(GO)截止误差如下式:
式中,w=h=2n,n为最大分解层数。
与现有技术相比,本发明提供了基于3D视觉检测技术的识别与检测方法,具备以下有益效果:
1.本发明的有益效果是:管道表面存在大量微小尺寸和微小形变缺陷,传统二维检测方法由于成像条件限制,对于这两类微小缺陷的检出率较低。本发明将立体三维测量方法应用于管道表面缺陷的三维在线检测,根据管道生产环境,在转辊一侧设置了线扫描方式的立体三维测量系统,采用入射角度±18°的红、绿单色光源在中间场范围进行照明,获得了对微小缺陷的良好成像效果。针对划伤、点状缺陷等微小尺寸缺陷存在光照盲区的问题,本发明提出的立体成像方案具有多照明通道优势,通过多个照明通道实现了盲区的互补,针对凹凸等微小形变缺陷边缘模糊、对比度低的问题,采用立体法可获得表面方向梯度和三维深度,在梯度和深度通道中缺陷区域更为明显,有助于提高微小形变缺陷的检测效果。对凹凸形变缺陷的测量实验表明立体三维测量方法的测量范围受图像噪声和入射角影响。对于形变量0.040-0.310mm的凹凸形变缺陷可实现定量测量。在立体三维测量系统的基础上,设计了快速缺陷检测算法。提取梯度通道的局部标准差和局部绝对差、深度通道的投影特征建立特征图。对特征图进行滤波和阈值分割提取缺陷区域,实现了快速有效的缺陷检测。
附图说明
图1为本发明提出的基于3D视觉检测技术的识别与检测方法的一具体实施例的立体三维测量系统的布置图;
图2为本发明提出的基于3D视觉检测技术的识别与检测方法的一具体实施例的流程图。
具体实施方式
下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。
在本发明的描述中,需要理解的是,术语“上”、“下”、“前”、“后”、“左”、“右”、“顶”、“底”、“内”、“外”等指示的方位或位置关系为基于附图所示的方位或位置关系,仅是为了便于描述本发明和简化描述,而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作,因此不能理解为对本发明的限制。
实施例1:
基于3D视觉检测技术的识别与检测方法,包括以下步骤:
S1、采用经过标定的对称的线性光源和线阵相机结合的线扫描三维测量系统进行扫描;
式中,e为光源功率系数,通过标定步骤可获得;
S4、将三维深度图沿管道旋转方向,分隔为宽度为d的纵条,计算纵条内曲面投影ZPR如下:
采用采用d/2步长分割纵条区域进行曲面投影,曲面投影ZPR最终拼接成与三维深度图等高度的投影图像;
在区域C内计算局部标准差QSD和局部绝对差QRA,计算公式如下:
S5、将S4中经曲面投影ZPR、局部标准差QSD和局部绝对差QRA的通道特征图,经过均值滤波后,采用自适应阈值可以进行初步的缺陷区域分割。考虑到分割区域的连续性,采用形态学闭运算闭合连通区域,最终实现缺陷的准确检出和定位。
进一步,优选地,所述S1中的对称的线性光源采用红、绿色线形LED光源构成,绿光波长为555-585nm,红光波长620-639nm,光源照明范围具有一定的重叠区域,入射角度为±18°。
进一步,优选地,所述S3中的构建三维深度图包括以下步骤:
A1、通过S1中的线扫描三维测量系统,获得三维表面的x、y方向的梯度测量矩阵P、Q,通过积分计算三维曲面公式为:
式中,r为积分路径;
A2、将三维重建问题看做对一个曲面的拟合,建立关于梯度、图像数据的泛函损失函数,公式如下:
式中,误差项J1是光度立体测量范围Ω内,梯度测量值p、q与梯度计Zx、Zy间的误差;J2为正则化项;J3为约束表面法向量的连续性,抑制了“折痕”类型的梯度突变特征;正则化系数λ、μ取值为0;
A3、从x和y方向的梯度矩阵P和Q重建三维深度矩阵Z,并对A2中的公式进行优化,得到:
式中包含了对方向梯度q的优化,同时含有曲面梯度的正则化项;
A4、采用WO算法构建三维深度图。
进一步,优选地,所述S4中的d为32像素。
进一步,优选地,所述S4中的区域C为8×6像素的矩形区域
进一步,优选地,所述A4采用WO算法构建三维深度图中加入迭代优化步骤(GO),其迭代优化步骤(GO)截止误差如下式:
式中,w=h=2n,n为最大分解层数。
实施例2:本实施例基于实施例1,但又有所不同的是,按照实施例1中的方式检测并与传统二维检测方式进行对比,对比结果如下表1;
表1缺陷检出率
由表1给出的缺陷检出率结果可见由于二维图像成像效果上的限制,部分缺陷在图像中处于照明盲区,二维检测方法对于各类缺陷的检出率均较低,但本发明的三维检测方法,各类缺陷的检出率均得到了提升,总体检出率提升13%;对于微小点状缺陷,由于多通道照明对盲区的互补效果,同时采用光度立体方法提高了部分处于观察盲区的微小缺陷对比度,检出率大幅提升;对于横向和斜向划伤缺陷,传统二维检测方法存在检出率低,缺陷区域检测不完全的情况,即仅能检出划伤缺陷部分区域。采用三维检测方法,横向划伤和倾斜划伤的检出率均有所提高,缺陷区域检测不完全的情况得到了明显改善;对于纵向划伤缺陷,由于入射光方向平行于划伤,LED条形光源对于此类划伤产生类似于无影灯的照明效果,在图像中为较细的暗条状,称为“暗划伤”。由于暗划伤的成像效果没有得到明显改善,其检出率提升较小,对于凹凸形变缺陷,传统二维检测方法仅能检测部分变形量较大、成像效果明显的测试样本,对于轻微的凹凸形变缺陷则无法有效检测。而在方向梯度和深度通道中,轻微凹凸形变缺陷具有较高的对比度,该类缺陷的检出率有较大提高。
以上所述,仅为本发明较佳的具体实施方式,但本发明的保护范围并不局限于此,任何熟悉本技术领域的技术人员在本发明揭露的技术范围内,根据本发明的技术方案及其发明构思加以等同替换或改变,都应涵盖在本发明的保护范围之内。
Claims (6)
1.基于3D视觉检测技术的识别与检测方法,其特征在于,包括以下步骤:
S1、采用经过标定的对称的线性光源和线阵相机结合的线扫描三维测量系统进行扫描;
式中,e为光源功率系数,通过标定步骤可获得;
S3、截取缺陷中心部位的相对灰度窄带,带入相对灰度I~公式中计算窄带部分的倾角截面曲线,并将计算的倾角截面直接转化成方向梯度Zxy,并构建三维深度图;
S4、将三维深度图沿管道旋转方向,分隔为宽度为d的纵条,计算纵条内曲面投影ZPR如下:
采用采用d/2步长分割纵条区域进行曲面投影,曲面投影ZPR最终拼接成与三维深度图等高度的投影图像;
在区域C内计算局部标准差QSD和局部绝对差QRA,计算公式如下:
S5、将S4中经曲面投影ZPR、局部标准差QSD和局部绝对差QRA的通道特征图,经过均值滤波后,采用自适应阈值可以进行初步的缺陷区域分割。考虑到分割区域的连续性,采用形态学闭运算闭合连通区域,最终实现缺陷的准确检出和定位。
2.根据权利要求1所述的基于3D视觉检测技术的识别与检测方法,其特征在于:所述S1中的对称的线性光源采用红、绿色线形LED光源构成,绿光波长为555-585nm,红光波长620-639nm,光源照明范围具有一定的重叠区域,入射角度为±18°。
3.根据权利要求1所述的基于3D视觉检测技术的识别与检测方法,其特征在于:所述S3中的构建三维深度图包括以下步骤:
A1、通过S1中的线扫描三维测量系统,获得三维表面的x、y方向的梯度测量矩阵P、Q,通过积分计算三维曲面公式为:
式中,r为积分路径;
A2、将三维重建问题看做对一个曲面的拟合,建立关于梯度、图像数据的泛函损失函数,公式如下:
式中,误差项J1是光度立体测量范围Ω内,梯度测量值p、q与梯度计Zx、Zy间的误差;J2为正则化项;J3为约束表面法向量的连续性,抑制了“折痕”类型的梯度突变特征;正则化系数λ、μ取值为0;
A3、从x和y方向的梯度矩阵P和Q重建三维深度矩阵Z,并对A2中的公式进行优化,得到:
式中包含了对方向梯度q的优化,同时含有曲面梯度的正则化项;
A4、采用WO算法构建三维深度图。
4.根据权利要求1所述的基于3D视觉检测技术的识别与检测方法,其特征在于:所述S4中的d为32像素。
5.根据权利要求1所述的基于3D视觉检测技术的识别与检测方法,其特征在于:所述S4中的区域C为8×6像素的矩形区域。
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