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This paper proposes a set of high-precision visual measurement methods to address the challenges of perspective distortion, thickness corner offset, and continuous tracking of multiple workpieces in the dynamic environment of intelligent manufacturing. In the preprocessing stage, the collected images are converted into approximately orthographic projection views through camera calibration and perspective correction. To obtain accurate edge images, this paper proposes an edge detection algorithm based on multi-scale edge fusion. By applying guided filtering to the collected images at different scales and then using dynamic Canny edge detection, the complete contour of the workpiece is obtained. To address the corner offset caused by the thickness of the workpiece, this paper proposes a high-precision corner extraction algorithm based on thickness interference elimination. By fusing sub-pixel corners and fitted corners, precise corner positioning is achieved. In addition, an object tracking algorithm is designed to match and identify the centroids of the workpieces, enabling automatic size recognition and measurement of multiple workpieces in consecutive frames. Experimental results show that this method can measure the sizes of multiple workpieces in arbitrary poses, with a mean error of 0.599 mm and a standard deviation of 0.172 mm, meeting the measurement requirements in industrial production.
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本文针对智能制造中动态环境下工件尺寸测量面临的透视畸变、厚度角点偏移以及多工件持续追踪等挑战,提出了一套高精度视觉测量方法。在预处理阶段,通过相机标定和透视矫正,将采集图像转换为近似正射投影视图。为了得到精确的边缘图像,本文提出一种基于多尺度边缘融合的边缘检测算法,通过对采集图像在不同尺度上进行引导滤波处理后再应用动态Canny边缘检测得到完整的工件轮廓。针对工件厚度引起的角点偏移,本文提出了基于厚度干扰消除的高精度角点提取算法,通过融合亚像素角点与拟合角点实现精确角点定位。此外,本文设计了对象追踪算法对工件质心进行匹配识别,实现了多工件在连续帧中的尺寸自动识别与测量。实验结果显示本方法能对多个任意位姿的工件进行尺寸测量,尺寸测量均值误差为0.599 mm,标准差为0.172 mm,满足工业生产中的测量需求。
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王佳希,硕士研究生,主要研究方向为图像处理、机器视觉等。E-mail: 1393207825@qq.com
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王佳希,硕士研究生,主要研究方向为图像处理、机器视觉等。E-mail: 1393207825@qq.com
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