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SCIENTIA SINICA Informationis, Volume 47 , Issue 4 : 455-467(2017) https://doi.org/10.1360/N112016-00174

Lane detection algorithm based on geometric moment sampling

More info
  • ReceivedJul 14, 2016
  • AcceptedOct 8, 2016
  • PublishedFeb 10, 2017

Abstract


Funded by

国家自然科学基金(61332015)

山东省自然科学基金(ZR2013FM032)

  • Figure 1

    Flow chart of proposed algorithm

  • Figure 2

    (Color online) Detection of the starting point for a multi-lane scenario

  • Figure 3

    Elimination of outliers for denoising. (a) Outliers are marked by squares before elimination; (b) result after elimination

  • Figure 4

    (Color online) Results on synthetic images used for testing. (a) Original image degraded by salt-and-pepper noise; (b) centroid detection; (c) lane detection

  • Figure 5

    (Color online) Detection results on inclined roads in the national vehicle testing ground under different lighting. (a) and (b) Original images; (c) and (d) detection results

  • Figure 6

    (Color online) Detection results for lanes with different shapes and interference. (a) Dotted lane; protectłinebreak (b) tilted road with dotted line on one side, full line on the other, and with large area of stains; (c) tunnel at night; (d) expressway at night; (e)$\sim$(h) detection results

  • Figure 7

    (Color online) Detection results for lanes with different binarization methods. (a) Original image; protectłinebreak (b) binarization for Bernsen [14]; (c) binarization for iteration [13]; (d) binarization for Otsu [11,12]; (e)$\sim$(h) detection results

  • Figure 8

    (Color online) Detection results for different detection algorithms. (a), (d) and (g) Original images; protectłinebreak (b) detection result for Hough transformation; (e) and (h) detection results for LS; (c), (f) and (i) detection results for proposed method

  • Table 1   Detection results for synthetic images with different types of noise
    Number of synthetic images Noise type Number of correct detection Correct detection rate (%)
    White Gaussian noise 96 91.43
    105 Salt and pepper noise 99 94.28
    Random noise 93 88.57
  • Table 2   Comparison of lane detection results for different binarization algorithms
    Number of images Binarization method Average processing time for binarization (s/frame) Number of correct detection images Correct detection rate (%)
    Bernsen 26.112 102 92.73
    110 Iteration 12.904 101 91.81
    Otsu 0.046 105 95.54
  • Table 3   Comparison of lane detection results for different detection algorithms
    Lane shape Number of images Detection method Average processing time (ms/frame) Number of correct detection images Correct detection rate (%)
    Straight 140 Hough transformation 906.30 109 77.86
    Proposed method 269.64 136 97.14
    Curve 150 LS 933.67 97 65.33
    Proposed method 376.89 139 94.67