基于角点和三角形内间距的多工件检测方法

Multi-Workpiece Detection Algorithm Based on Corner Points and Triangular Centroid Distances

针对堆叠条件下工件的视觉检测问题,该文提出了一种基于角点特征信息的三角形内间距(Triangular Centroid Distances,TCDs)描述子。首先,对目标局部轮廓角点和方向进行检测;然后,基于检测到的角点和方向信息在模板轮廓上生成疑似轮廓段;最后,对目标轮廓和模板上的疑似轮廓段提取改进后的描述子特征矩阵,并计算目标轮廓矩阵与各疑似轮廓特征矩阵之间的距离,其中距离最小的疑似轮廓段即为目标轮廓段在模板轮廓段上的匹配结果。实验结果表明,在相同取样点的情况下,所提出的算法不仅识别准确率优于传统三角形内间距算法,而且计算效率也大幅提升。

In this paper,a triangular centroid distances(TCDs)descriptor that integrates corner information is proposed to solve the detection problem in the case of overlapping workpieces.The proposed descriptor detects the corner points and local contour direction of the target,which can be used to detect the suspected contour segments on the template contour.Then,the improved TCDs feature matrices are extracted from the target contour and the suspected contour segments of the templates.Finally,by calculating the distances between target contour matrix and each suspected contour feature matrix,the suspicious contour segment with the smallest distance can be determined as the corresponding workpiece.The experimental results showed that,not only recognition rate but also the efficiency of the proposed algorithm can be improved compared with traditional TCDs algorithm.