The location of target corner points in an image is the key data for implementing many computer vision tasks. In order to overcome the data redundancy problem arising from traditional detection algorithms, an edge-based corner point target detection method in the scale space is proposed. First, a grouped multilayer scale space is constructed, and multiple smoothed images are obtained after projecting the original image into it. At the same time, the defined edge operator is applied to detect all edges in the smoothed image to obtain multiple sets of pixel points stored in order, and the transformation to larger scales is stopped when the number of point sets is stable. Then, at the current scale, the indicator values of each element in the point set reflecting its corner intensity are calculated. The support set interval of corner points is detected according to the variation pattern of these indicator values, and the final target corner points are determined in this interval using a Gaussian fitting function. Experiments show that the method is able to detect the target corner points with significant features and their angles, where the accuracy of the synthesized images is at the pixel level and the average error to figure ratio in the application case is about 1.5/100.