Abstract: To address the issue of low calibration accuracy in traditional camera calibration methods and edge blurring under strong environmental interference, which cannot meet the requirements of high-precision 3D reconstruction, an improved Canny edge detection algorithm was proposed to enhance calibration accuracy. First, the calibration images were processed using Gaussian filtering and guided filtering to eliminate environmental noise while maintaining edge integrity and smoothness. Then, a four-directional convolution template-based Sobel operator was employed to calculate edge gradients, improving gradient computation accuracy and preventing edge detail loss. Finally, the Otsu algorithm was utilized to adaptively determine edge thresholds, enhancing the algorithm’s adaptability to threshold detection, and Zhang’s calibration method was applied to complete the image calibration. To verify the effectiveness and robustness of the proposed algorithm, Gaussian noise with a standard deviation of 30 and salt-and-pepper noise with a density of 20% were added to 20 calibration board images to simulate a high-interference environment, and calibration experiments were conducted. The results demonstrate that compared with traditional and improved Canny algorithms, the proposed algorithm exhibits significant suppression effects on both Gaussian noise and salt-and-pepper noise while achieving superior edge extraction quality, reducing the camera calibration reprojection errors by 54.1% and 32.5% respectively. In standard workpiece measurement tests, the proposed method maintains mean absolute errors within 0.1–0.3 mm range with higher measurement accuracy and smaller root mean square errors (0.15–0.30 mm), demonstrating excellent engineering application value.