Automated Lane Marking Identification Based on Improved Canny Edge Detection Algorithm
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摘要: 在自动化道路病害检测过程中，检测车的横向偏移会导致路面病害横向定位的误差，通过边缘检测算法对标线进行自动识别及定位，可消除病害横向定位的误差. 但传统Canny边缘检测算法存在对标线边缘识别不全，噪声、伪边缘较多的问题. 因此本文对Canny算法进行改进：采用维纳滤波替代高斯滤波以滤除噪声；利用OTSU法自适应地选取图像阈值；提出划定准偏区域以滤除路面干扰并提高边缘完整识别率. 研究结果表明：与其他边缘检测算法相比，该算法检测出的边缘像素总数分布在900～2 000，与标准值1 352较为符合，8连通域数与边缘像素总数、4连通域数的比值分别分布在0～0.04和0～0.29，在抑制噪声、剔除伪边缘及路面干扰、边缘识别连续性和单一边缘响应方面实际应用效果较好；人工法验证结果表明该算法定位标线结果与实际值的吻合度高达99%，可在同步采集的3D激光图像上进行路面病害横向定位.Abstract: In a pavement distress survey, a wandering survey vehicle would reduce the accuracy of lateral position measurement of pavement distress. The errors in the measurement of distress lateral position can be eliminated based on the lane marking identified using an edge detection algorithm. However, the traditional edge detection algorithm has some limitations, such as incomplete and false edge recognition. Therefore, an improved Canny edge detection algorithm is proposed herein, which is composed of three steps: noise elimination using a Wiener filter, adaptive threshold definition using Otsu’s method, and lane marking edge recognition based on offset-allowed area method. The test results show that the total number of edge pixels detected by the proposed algorithm is between 900 and 2 000, which is close to the standard value of 1 352 for existing edge detection algorithms. The ratio of the number of eight-connected domains to the total number of edge pixels ranges from 0 to 0.04, and the ratio of the number of eight-connected domains to the number of four-connected domains ranges from 0 to 0.29, which shows that the improved algorithm demonstrates excellent performance in terms of noise elimination, false edge recognition, consistent edge detection, and single edge response. A validation test based on the manual method shows that the correlation between the test value and reference is up to 99%. The proposed method can be used to detect the lateral position of pavement distress based on 3D laser imaging data.
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Key words:
- edge detection /
- image processing /
- improved Canny algorithm /
- automatic lane marking
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图 2 人工设置阈值和OTSU法处理结果对比举例
Figure 2. Example of comparison between man-made threshold and Otsu’s threshold
图 3 未划定准偏区域与划定准偏区域处理结果对比举例
Figure 3. Example of comparison between no offset-allowed area and offset-allowed area
图 5 边缘检测算法处理结果边缘像素数量对比
Figure 5. Comparison of edge pixel quantity based on four edge detection algorithms
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