基于曲率相似性的路面连续纵长裂缝匹配方法

陈实; 黄玉春

doi:10.3963/j.jssn.1674-4861.2022.04.013

基于曲率相似性的路面连续纵长裂缝匹配方法

doi: 10.3963/j.jssn.1674-4861.2022.04.013

陈实,
黄玉春^,

武汉大学遥感信息工程学院武汉 430079

基金项目:

国家自然科学基金项目 41671419

详细信息

作者简介:
陈实（1996—），硕士研究生. 研究方向：交通遥感、移动测量. E-mail：2550686663@qq.com

通讯作者:
黄玉春（1977—），博士，副教授. 研究方向：交通遥感、移动测量. E-mail：hycwhu@whu.edu.cn

中图分类号: U491.5+4
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出版历程
- 收稿日期: 2022-04-21
- 网络出版日期: 2022-09-17

A Matching Method for Longitudinal Cracks Based on Curvature Similarity

CHEN Shi,
HUANG Yuchun^,

School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

摘要

摘要: 车载相机拍摄得到的路面裂缝形状分布随机，且由于视场角有限每次只能拍摄到道路上纵向长裂缝的一部分，导致纵长裂缝检测不完整。利用逆透视变换方法将车载相机采集的道路前方倾斜图像转化成正射图像，以去除纵长裂缝图像的透视变形；采用深度学习中的语义分割网络Deeplab V3+实现裂缝像素的提取；在此基础上，提出基于曲率相似性的由粗到精的两阶段路面连续纵长裂缝匹配方法。将待匹配的裂缝曲线分割为一连串相互重叠的子曲线序列，相互匹配的子曲线即为裂缝曲线相匹配的部分；利用曲率将子曲线局部形状与走势的特征表达为描述符，使用Kd-tree最邻近匹配算法对曲线描述符进行快速粗匹配。根据连续2张道路图像中纵长裂缝在空间位置分布上延续的特征，在裂缝曲线分割成子曲线时添加约束条件，前1张图像中裂缝曲线的起点和后1张图像中裂缝曲线的终点分别作为各自子曲线的1个端点；在粗匹配结果的基础上，逐步缩小分割曲线的间隔，迭代提高子曲线描述符间的归一化互相关系数，直至其大于等于阈值或者迭代次数超出最大迭代次数，实现对粗匹配结果的精调整。为验证算法精度，以武汉大学校园内路面不同类型的连续纵长裂缝为对象开展实验，匹配结果误差最小为0.688像素，精调整的误差比粗匹配平均减小24.19%。为进一步验证噪声下干扰的稳定性，仿真环境下增加了裂纹像素噪声；当高斯噪声的标准差从0增大到2像素时，匹配结果误差仅增大了1.083像素。将所提方法与SIFT算法进行对比，10组实验中，所提方法都能匹配成功；而SIFT算法在其中2组实验中匹配结果完全错误，表明所提算法有较好稳定性。
- 路面养护 /
- 纵长裂缝 /
- 透视变换 /
- 曲线匹配 /
- 曲率相似性
Abstract: Pavement cracks captured byon-board cameras are distributed randomly in shapes, and only a part of the longitudinal cracks on the roads can be captured each timedue to the limited field of view, resulting in incomplete detection of longitudinal cracks. The imagesacquired by the on-board cameras are transformed from oblique images intoorthographic images by using the inverse perspective transformation method, thus the perspective distortionof the longitudinal cracks are corrected. Then a deep learning based semantic segmentation network, Deeplab V3+, is used to extract the pixels of cracks. Based on curvature similarity, a two-stage method from coarse to fineis proposed for matching longitudinal cracks.The crack curve to be matched is divided into a sequence of overlapping sub-curves, which are characterized by descriptor of curvature, and the matched sub-curves are the matched parts of cracks. The curvature is used to express the local shape and trend features of sub-curvesas descriptors, then the Kd-tree nearest neighbor matching algorithm is used to perform coarse and fast matching of thedescriptors. According to the spatial distribution of longitudinal cracks in two consecutive road images, constraints are added when the crack curves are divided into sub-curves, the starting point of the crack curve in previousimage and the ending point in the next image areused asterminus of each respective sub-curve. Based on the results of coarse matching, the interval of segmentation curves is gradually reduced, and the normalized cross-correlation coefficient is iteratively improved until it is greater than or equal to the threshold or the number of iterations exceeds the maximum value to achieve fine adjustment of the results of coarse matching. To verify the accuracy of the algorithm, a case study is carried out with different types of continuous and longitudinal cracks on the campus roads of Wuhan University.The minimum error of the matching results can reach 0.688 pixels. Compared with the coarse matching, the error after fine adjustmentreduces by 24.19% on average. In order to further verify the stability of the algorithm under noise, crack pixel noise is added to the simulation environment.When the standard deviation of Gaussian noise increases from 0 to 2 pixels, the error of the matching results increases by only 1.083 pixels. Compared with the SIFT algorithm, the proposed method can achieve successful matching in all ten groups of experiments, while the matching results of the SIFT algorithm completely fails in two groups. It indicates that the algorithm proposed has better stability under normal and noise environment.
- pavement maintenance /
- longitudinal crack /
- perspective transformation /
- curve matching /
- curvature similarity

HTML全文

图 1 相机坐标系与世界坐标系

Figure 1. Camera coordinate system and world coordinate system

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图 2 曲线曲率特征

Figure 2. Curvature characteristics of curve

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图 3 重采样方法

Figure 3. Resampling method

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图 4 曲线匹配示意图

Figure 4. Diagram of curve matching

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图 5 基于K-d tree最邻近裂缝曲线粗匹配算法

Figure 5. Crack curve nearest matching algorithm based on K-d tree

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图 6 精调整起始位置流程

Figure 6. Process of fine adjust starting position

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图 7 粗匹配与精调整裂缝匹配误差

Figure 7. Matching error of coarse matching and fine adjustment

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图 8 简单裂缝实验结果

Figure 8. Results of simple crack

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图 9 复杂裂缝实验结果

Figure 9. Results of complex crack

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图 10 仿真实验曲线匹配误差

Figure 10. Error of curve matching in simulation experiment

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图 11 仿真实验结果

Figure 11. Results of simulation experimen

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图 12 SIFT算法匹配结果

Figure 12. Matching results of SIFT algorithm

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