In order to meet the requirement of high accuracy and strong antinoise performance of image edge localization in computer vision calibration and precision measurement, a subpixel level image edge detection algorithm based on Franklin moments is proposed. Firstly, a subpixel edge model is established to extract the detailed features of image edge points with the convolution of Franklin moments at all levels. Then, according to the rotation invariance principle of Franklin moments, the relationship among different levels of Franklin moments after image edge rotating to the vertical direction is analyzed, and the key parameters of the subpixel image edge are determined. Finally, the actual subpixel edge points in the image are located based on the improved edge judgment condition. A large number of experimental results show that compared with Zernike moment subpixel level image edge detection algorithm, the subpixel level image edge detection algorithm combining wavelet transform with Zernike moment, and the subpixel level image edge detection algorithm combining Roberts operator with Zernike moment, the proposed subpixel level image edge detection algorithm based on Franklin moments possesses higher speed and accuracy and stronger antinoise performance, which better meets the measurement requirements of stability and reliability and high precision in image edge localization.