Application of an Efficient AKAZE Feature Detection and Matching Algorithm in Image Recognition

This study proposes an improved N-AKAZE algorithm, which systematically optimizes the efficiency bottleneck of the traditional AKAZE algorithm in the feature matching of bronze drum images. By introducing seven key technical improvements, including Adaptive Scale Selection (ASS), Edge-Preserving Diffusion (EPD), and Multi-Threshold Detection (MTD), a complete algorithm framework with Bidirectional Matching Verification (BMV) is constructed. Experiments show that the average feature point detection time on the bronze drum image dataset is reduced to 85.96ms (p<0.01), and the matching accuracy is improved by 37.2%. This has significant advantages over the traditional AKAZE algorithm (t=5.32, df=12, p=0.0002). The research results provide a new technical paradigm for the digital preservation of cultural heritage.