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In order to solve the problems of large amount of calculation and slow matching speed in the traditional point feature matching methods, an image matching algorithm based on CenSurE-star and LDB is proposed, which can be used to quickly match the target images in visual detection. Firstly, the size of the filter is adjusted to fast detect the CenSurE-star feature points of different scales in the target image. Then, the LDB method is used to describe the feature points combined with their neighborhood. The similarity between the image feature points is measured by the Hamming distance between descriptors, and the corresponding filtering is carried out. Finally, RANSAC is used to eliminate the remaining mismatches and achieve the accurate matching between the images. Experimental research shows that compared with common algorithms such as SIFT and SURF in the matching of the three sets of target images with regard to illumination, noise and blur changes, this algorithm not only improves the matching speed, but also ensures high matching accuracy.