基于孔特征的弱纹理堆叠工件识别

doi:10.3778/j.issn.1002-8331.2008-0248

摘要/Abstract

摘要： 针对目前工业现场弱纹理堆叠工件识别困难的问题，提出一种以工件表面孔洞为特征的改进几何模板匹配算法，以合页为例进行工件识别。首先采用加权平均法对彩色图像进行灰度化处理，再采用Canny算法进行边缘检测；其次采用旋转卡壳算法求取轮廓的最小面积外接矩形，进行几何约束后得到孔洞对应的孔轮廓，并采用随机增量法计算孔轮廓的最小外接圆得到孔特征圆心坐标。采用提出的改进几何模板匹配算法，即根据孔特征之间的几何约束进行工件识别，根据孔特征之间是否存在边缘剔除误识别工件。实验结果表明，提出的算法对带孔弱纹理堆叠工件具有良好的识别效果，工件识别查全率为98.3%，误检率为0.9%，为带孔弱纹理工件的识别提供方法。

关键词: 弱纹理堆叠工件, 工件识别, 孔特征, 改进几何模板匹配, 几何约束

Abstract: Aiming at the problem of difficult recognition for stacked texture-less workpieces in industrial field, an improved geometric template matching algorithm featuring holes on the workpiece surface is proposed, and the hinge is taken as an example for workpiece recognition. The weighted average method is used to gray-scale the color image, and then the Canny algorithm is used for edge detection. The rotating calipers algorithm is used to obtain the minimum area bounding rectangle of the contour, and geometric constraints are performed to obtain the hole contour corresponding to the hole, and randomized incremental algorithm calculates the minimum enclosing circle of the hole contour to obtain the coordinates of hole feature center. The proposed improved geometric template matching algorithm is used to identify the workpieces according to the geometric constraints between the hole features. The fake workpieces are removed according to whether there is edge between the hole features. The experimental results show that the proposed algorithm has a good recognition effect on stacked texture-less workpieces with holes. The recall rate of workpieces recognition is 98.3% and the false positive rate is 0.9%. It provides a method for the recognition of texture-less workpieces with holes.

Key words: stacked texture-less workpieces, workpiece recognition, hole feature, improved geometric template matching, geometry constraint

王书宇, 田建艳, 蔡文站, 李济甫, 王素钢, 李丽宏. 基于孔特征的弱纹理堆叠工件识别[J]. 计算机工程与应用, 2022, 58(3): 282-288.

WANG Shuyu, TIAN Jianyan, CAI Wenzhan, LI Jifu, WANG Sugang, LI Lihong. Recognition of Stacked Texture-Less Workpieces Based on Hole Features[J]. Computer Engineering and Applications, 2022, 58(3): 282-288.

参考文献

[1] 贾丙西，刘山，张凯祥，等.机器人视觉伺服研究进展：视觉系统与控制策略[J].自动化学报，2015，41（5）：861-873.
JIA B X，LIU S，ZHANG K X.Survey on robot visual servo control：vision system and control strategies[J].Acta Automatica Sinica，2015，41（5）：861-873.
[2] ROBLA-GOMEZ S，BECERRA V M，LLATA J R，et al.Working together：a review on safe human-robot collaboration in industrial environments[J].IEEE Access，2017，5：26754-26773.
[3] ZHONG B，LI Y.Image feature point matching based on improved SIFT algorithm[C]//Proceedings of the 4th International Conference on Image，Vision and Computing，Xiamen，July 5-7，2019.Piscataway：IEEE，2019：489-493.
[4] BI S，CHAI Z，LIU C，et al.A segmentation-driven approach for 6D object pose estimation in the crowd[C]//Proceedings of the 2019 International Conference on Advanced Intelligent Mechatronics，Hong Kong，July 8-12，2019.Piscataway：IEEE，2019：19-24.
[5] RUSU R B，BLODOW N，BEETZ M.Fast point feature histograms（FPFH） for 3D registration[C]//Proceedings of the 2009 IEEE International Conference on Robotics and Automation，Kobe，May 12-17，2009.Piscataway：IEEE，2009：3212-3217.
[6] ZHANG H，CAO Q.Detect in RGB，optimize in edge：accurate 6D pose estimation for texture-less industrial parts[C]//Proceedings of the 2019 International Conference on Robotics and Automation，Montreal，May 20-24，2019.Piscataway：IEEE，2019：3486-3492.
[7] 田明锐，胡永彪，金守峰.结合聚类参数的圆投影模板匹配改进算法[J].计算机工程与应用，2015，51（21）：177-184.
TIAN M R，HU Y B，JIN S F.Improved method on RPT template matching by clustering parameters[J].Computer Engineering and Applications，2015，51（21）：177-184.
[8] LIU F，GONG C，HUANG X，et al.Robust visual tracking revisited：from correlation filter to template matching[J].IEEE Transactions on Image Processing，2018，27（6）：2777-2790.
[9] 齐美星，孙伟.基于金字塔图像结构与Hu高阶矩的螺丝目标匹配算法[J].组合机床与自动化加工技术，2017（9）：19-22.
QI M X，SUN W.Screw target matching algorithm based on image pyramid structure and Hu moment[J].Modular Machine Tool ＆ Automatic Manufacturing Technique，2017（9）：19-22.
[10] 杨梅，谭泽富，蔡黎.基于自适应形状上下文特征的实时杆号识别[J].计算机工程与应用，2018，54（12）：187-191.
YANG M，TAN Z F，CAI L.Real-time recognition of pole number based on adaptive shape context feature[J].Computer Engineering and Applications，2018，54（12）：187-191.
[11] 吴晓军，邹广华.基于边缘几何特征的高性能模板匹配算法[J].仪器仪表学报，2013，34（7）：1462-1469.
WU X J，ZOU G H.High performance template matching algorithm based on edge geometric features[J].Chinese Journal of Scientific Instrument，2013，34（7）：1462-1469.
[12] HE Z，JIANG Z，ZHAO X，et al.Sparse template-based 6-D pose estimation of metal parts using a monocular camera[J].IEEE Transactions on Industrial Electronics，2020，67（1）：390-401.
[13] LI C G，CHANG Y.Automated visual positioning and precision placement of a workpiece using deep learning[J].The International Journal of Advanced Manufacturing Technology，2019，104（9/12）：4527-4538.
[14] 王青，郑飞，任英武，等.基于孔特征约束的飞机部件位姿优化方法[J].计算机集成制造系统，2017，23（2）：243-252.
WANG Q，ZHENG F，REN Y W.Posture evaluation method for aircraft component based on hole feature[J].Computer Integrated Manufacturing Systems，2017，23（2）：243-252.
[15] 顾梅花，苏彬彬，王苗苗，等.彩色图像灰度化算法综述[J].计算机应用研究，2019，36（5）：1286-1292.
GU M H，SU B B，WANG M M.Survey on decolorization methods[J].Application Research of Computers，2019，36（5）：1286-1292.
[16] 张亚洲，卢先领.基于改进相干增强扩散与纹理能量测度和高斯混合模型的导光板表面缺陷检测方法[J].计算机应用，2020，40（5）：1545-1552.
ZHANG Y Z，LU X L.Surface defect detection method of light-guide plate based on improved coherence enhancing diffusion and texture energy measure-Gaussian mixture model[J].Journal of Computer Applications，2020，40（5）：1545-1552.