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The local binary descriptors in AKAZE algorithm are not sensitive to image scale change and fuzzy change, which leads to uneven feature point extraction and low accuracy of feature matching. An improved feature matching algorithm combining AKAZE and MAGSAC was thus proposed. In this algorithm, the FREAK descriptor was used to describe the feature points, and the main direction of the feature points was determined by calculating the gradient of the sample points. The MAGSAC method was used to eliminate the wrong match points. The experimental results showed that the matching accuracy of the improved algorithm was 6. 98% higher than that of the traditional AKAZE algorithm when the image changed in scale and rotation, and the average time of feature point extraction was 0. 084 ms less than that of the traditional AKAZE algorithm when the image changed in scale; the matching accuracy was 8. 57% higher than that of the traditional AKAZE algorithm when the image changed in blur, and the average time of feature point extraction was 0. 05 ms less than that of the traditional AKAZE algorithm. [ABSTRACT FROM AUTHOR]