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Edge detection is a very important and practical image processing method in computer vision, which is used in various fields. However, in the process of image acquisition or transmission, due to the interference of the external environment, it is easy to have a low detection rate of the edge of the result or the phenomenon of pseudo-edge, and scholars have proposed many improvement methods for this. However, there are few general edge detection methods, and existing algorithms are aimed at dealing with problems in specific scenarios or situations. Kirsch's RGB image edge detection algorithm combined with high and low double thresholds is proposed to solve the above problems. Firstly, the different component maps under the RGB color space of the original image are extracted, and the edge intensity is obtained by using the improved Kirsch operator for each component map. Then, the high and low double thresholds are used to divide the edge points and background points of the image to obtain the edge results of different color spaces. Finally, the edge detection results of different components are fused to obtain the final edge results. The experimental results show that compared with other algorithms, the proposed algorithm has clearer edges, more complete details, better edge coherence, higher detection rate and wider application range than other algorithms.