Automated Lane Marking Identification Based on Improved Canny Edge Detection Algorithm

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.