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.