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基于SIFT算法和改进的RANSAC算法对森林火灾的图像识别与试验研究

Image Recognition and Experimental Research on Forest Fire Based on SIFT Algorithm and Improved RANSAC Algorithm

  • 摘要: 针对森林火灾的图像识别中遇到的误检测和漏检测等问题,提出一种SIFT(尺度不变特征变换)算法和改进后的RANSAC(随机抽样一致)算法。该算法能够大幅度地提高匹配精度以及缩短匹配时间。该文首先用SIFT算法提取图片中的特征点,然后通过降低RANSAC内点集的个数,使用改进后的RANSAC算法对这些特征点进行处理,去除掉错误匹配的点,以实现准确匹配。通过点烧实验得到数据集,使用MATLAB进行仿真,对比不同算法的匹配时间以及匹配精度,证明改进后的RANSAC算法使其匹配精度平均提高了11%,匹配时间平均缩短了4.8 s。研究证明改进后的RANSAC算法确实对森林火灾的图像识别具有提升检测效率的作用。

     

    Abstract: A SIFT(scale invariant feature Transform) algorithm and an improved RANSAC(random sampling consistency) algorithm were proposed to solve the problem of error detection and miss detection in forest fire image recognition. The algorithm can greatly improve the matching accuracy and shorten the matching time. In this paper, SIFT algorithm was firstly used to extract feature points in the image, and then by reducing the number of points set in RANSAC, the improved RANSAC algorithm was used to process these feature points and remove the mismatched points to achieve accurate matching. The data set was obtained through burning experiment and simulated by MATLAB. By comparing the matching time and matching accuracy of different algorithms, it was proved that the improved RANSAC algorithm in this paper had the effect of improving detection efficiency for forest fire image recognition.

     

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