面向智能运检管理的变电站运动目标立体匹配算法研究

针对目前变电站运动目标的立体匹配算法存在匹配点少、误匹配等问题,提出一种结合A-KAZE(Accelerated KAZE)算法和改进的SURF(Speeded Up Robust Features)算法的智能变电站运动目标立体匹配算法。采用A-KAZE算法用于提取两个图像的匹配特征点,利用二阶多尺度改进的SURF特征向量进一步计算二次响应,采用高阈值算法增加匹配点,随机采样一致算法消除不匹配点,完成匹配工作。通过实验比较,验证了该算法的有效性。实验结果表明,相对于未改进前匹配点对从908对提高到1 202对,匹配准确率从92.51%提高到96.17%,具有一定的实用价值。

Research on a stereo matching algorithm for substation moving goal oriented to intelligent transportation inspection management

Among the current stereo matching algorithms for moving targets in substations, there are a few matching points and mismatching problems. This paper proposes a three-dimensional matching algorithm for moving targets in smart substations that combines the A-KAZE algorithm and the improved Speeded Up Robust Features (SURF) algorithm. The A-KAZE algorithm is used to extract the matching feature points of two images, and second-order multi-scale heating is added to the SURF feature vector to further calculate the secondary response. A high threshold algorithm is used to increase the matching points, and the random sampling consensus algorithm eliminates the mismatch points to complete the matching. The effectiveness of the algorithm is verified through experimental comparison. The experimental results show that, compared with the previous matching point pairs from 908 to 1202 pairs, the matching accuracy is increased from 92.51% to 96.17%, which has definite practical value. This work is supported by the Science and Technology Project of Fujian Province (No. 52130A170006).