In this study, the Canny edge detection algorithm is proposed and applied to the image measurement field to solve the problems of image edge smoothing due to Gaussian filtering, poor self-adaptability of the threshold caused by artificially setting high and low thresholds, and poor removal effect caused by using the double threshold method to remove the false edge. First, the switching median filter instead of the Gaussian filter is used. The gray value of non-noise pixels is kept unchanged while denoising to improve the edge positioning accuracy. Next, the K-means clustering algorithm is employed to obtain the clustering center of the high and low gradient values. The OTSU algorithm is employed to acquire the gradient threshold value. The self-adaptation of the high and low threshold values could be achieved by combining the two methods. Finally, the interference edge of the image is removed by area morphology. The experimental results show that the improved algorithm has the advantages of high positioning accuracy, strong self-adaptability, and good removal effect of disturbance points.