基于特征增强的局部动态阈值ORB算法
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西南科技大学

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TP391.41

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基金项目:基于5G及机器人应用的精密电子制造智能化车间项目(20ZD3135)


Local Dynamic Threshold ORB Algorithm Based on Feature Enhancement
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Southwest University of Science and Technology

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    摘要:

    ORB(Oriented FAST and Rotated BRIEF)特征检测算法,在模糊场景和光照变化剧烈的环境中,容易使提取的特征点的数量和匹配的正确率出现巨大的差异,同时,在图像物体的拐角处也容易出现特征点的堆叠。针对这一情况,本文提出了一种改进的ORB特征检测算法。首先使用MSR(Multi-Scale Retinex)算法对图像进行特征增强。然后,对图像进行网格划分,针对每个网格的灰度分布情况调整特征点检测时的阈值,之后采取动态区域非极大值抑制方法筛选最佳特征点。实验结果表明,相较于原ORB算法,本文改进后的算法提取的特征点在图像上的分布更加均匀。当亮度在80%的范围内变化时,特征点的重复率稳定在75%以上,匹配正确率平均提高了22%

    Abstract:

    The ORB (Oriented FAST and Rotated BRIEF) feature detection algorithm often encounters challenges in envi-ronments with blur and drastic lighting variations, leading to significant disparities in the number of extracted features and the matching accuracy. Moreover, it tends to generate feature point clusters, particularly at image object corners. To address these issues, this paper proposes an improved ORB feature detection algorithm . Firstly, the Multi-Scale Retinex (MSR) algorithm is employed to enhance image features. Next, the image is divided into a grid, and thresholds for feature point detection are adjusted based on the grayscale distribution within each grid. Sub-sequently, a dynamic region-based non-maximum suppression method is applied to select the best feature points. Experimental results demonstrate that the algorithm, as improved in this paper, results in a more evenly distributed feature point layout on the image. In scenarios with lighting variations within an 80% range, the repeatability rate of feature points remains stable at over 75%, and the average matching accuracy improves by 22% compared to the original ORB algorithm.

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熊鑫,唐强,臧红彬. 基于特征增强的局部动态阈值ORB算法[J]. 科学技术与工程, , ():

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  • 收稿日期:2023-09-26
  • 最后修改日期:2024-05-21
  • 录用日期:2024-05-21
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