基于栅格填充的激光清洗轮廓规则化方法

doi:10.12382/bgxb.2024.0185

摘要/Abstract

摘要：

清洗区域的准确定位是减少清洗面积、提高清洗效率的关键因素之一。为解决激光清洗应用中不规则漆层区域定位的问题,提出一种基于栅格填充的轮廓规则化方法。通过基于L×a×b×(L×分量表示亮度,a×分量表示绿色~红色,b×分量表示蓝色~黄色)色彩空间的K-means图像分割方法,得到漆层区域图像;将漆层区域图像栅格化,确定漆层区域外部栅格顶点,并使用4邻域边界跟踪算法按照一定的判断准则搜寻漆层区域外部栅格顶点;将搜寻到的栅格顶点依次连接作为规则化形状,删除规则化形状的多余点和自相交部分,并以交并比(Intersection Over Union,IOU)为指标评价不同栅格宽度下的规则化形状,选取单个栅格面积与漆层区域面积比值范围在0.025~0.040内最大IOU所对应的栅格宽度,将此栅格宽度生成的直角多边形作为最优规则化形状。多种轮廓进行规则化对比分析结果表明,基于栅格填充的轮廓规则化方法得到的直角多边形相较于最小外接矩形,能更准确地表达漆层轮廓形状,减少清洗面积。

关键词: 激光清洗, 轮廓规则化, 图像分割, 边界跟踪, 机器视觉

Abstract:

The accurate positioning of cleaning area is one of the key factors to reduce the cleaning area and improve the cleaning efficiency.In order to solve the problem of positioning an irregular paint layer area in laser cleaning applications,a outlines regularization method based on grid filling is proposed.The paint layer region image is obtained by the K-means image segmentation method based on L×a×b× color space.The paint layer region image is rasterized to determine the external raster vertices of the paint layer region,and the 4-neighborhood boundary tracking algorithm is used to search for the external raster vertices of the paint layer region in accordance with certain judgment criteria.The searched raster vertices are sequentially connected as regularized shapes,the redundant points and self-intersecting parts of the regularized shapes are deleted,and the regularized shapes with different raster widths are evaluated by using the intersection over union (IOU) as an index,and the raster width corresponding to the largest IOU at the ratio of the area of a single raster to the area of lacquer layer region ranging from 0.025 to 0.04 is selected,and the right-angled polygons generated by this raster width are considered to be the optimal regularized shapes.The regularizations of various contours are compared and analyzed.The results show that the right-angled polygon obtained by the contour regularization method based on raster filling can more accurately express the contour shape of the paint layer and reduce the cleaning area compared with the minimum outer rectangle.

Key words: laser cleaning, outlines regularization, image segmentation, boundary tracking, machine vision

中图分类号:

TN249

刘伟军, 李强, 刘帅, 张凯, 邢飞. 基于栅格填充的激光清洗轮廓规则化方法[J]. 兵工学报, 2025, 46(3): 240185-.

LIU Weijun, LI Qiang, LIU Shuai, ZHANG Kai, XING Fei. Outlines Regularization Method for Laser Cleaning Based on Grid Filling[J]. Acta Armamentarii, 2025, 46(3): 240185-.

图/表 14

表1 2A12铝合金主要化学成分

Table 1 Main chemical composition of 2A12 aluminum alloy

化学成分	质量分数/%
Si	≤0.5
Fe	0.5
Cu	3.8~4.9
Mn	0.3~0.9
Mg	1.2~1.8
Ni	0.1
Zn	≤0.3
Ti	≤0.15
Al	余量

图1 复合漆层试样截面图

Fig.1 Cross-section of composite paint layer specimen

图2 激光清洗及视觉检测系统

Fig.2 Laser cleaning and visual inspection system

图3 不同工作路径的光斑搭接率示意图

Fig.3 Schematic diagrams of spot overlap rates for different working paths

表2 激光清洗灰色底漆工艺参数

Table 2 Process parameters of laser cleaning the gray primer

工艺参数	数值
激光能量密度/(J·cm^-2)	7.96
重复频率/kHz	20
脉宽/ns	60
激光光斑直径/mm	0.4
机械臂扫描速度/(mm·s^-1)	1
振镜扫描速度/(mm·s^-1)	3000~6000

图4 L×a×b×色彩空间下K-Means图像分割示意图

Fig.4 Schematic diagram of K-Means image segmentation in L×a×b× color space

图5 判断栅格顶点在漆层区域内或外

Fig.5 Determining whether a grid vertices is inside or outside the paint area

图6 栅格顶点位置关系示意图

Fig.6 Schematic diagram of grid vertices position relationship

图7 判断准则的4种情况

Fig.7 Four cases of judging criteria

图8 规则化过程中需要优化的问题

Fig.8 Problems to be optimized during regularization

图9 实验样本的面积比(左)与规则化形状(右)

Fig.9 Area ratios(left) and regularized shapes(right)of experimental samples

图10 6种样本的面积比(左)与规则化形状(右)

Fig.10 Area ratios(left) and regularized shapes(right)of 6 samples

图11 不同规则化方法对比

Fig.11 Comparison of different regularization methods

图12 规则化结果与清洗效果

Fig.12 Regularized results and cleaning effect

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