Lane Detection Based on Improved Canny Detector and Least Square Fitting

Abstract:

The video images of road monitoring system contain noise, which blurs the difference between the lane and the background. The lane detection algorithm based on traditional Canny edge detector hardly detects the single-pixel lane accurately and it produces pseudo lane. The paper proposes an effective lane detection method based on improved Canny edge detector and least square fitting. The proposed method improves the dual-threshold selection of traditional Canny detector by using the histogram concavity analysis, which sets the optimal threshold automatically. The least square method is used to fit the feature points of detected edges to accurate and single-pixel wide lane. Experimental results show that the proposed method detects the lane of video images accurately in the noise environment.

• Periodical: Advanced Materials Research (Volumes 765-767)
• Pages: 2383-2387
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.765-767.2383
• Online since: September 2013
• Authors: Guang Hua Chen, Wen Zhou, Feng Jiao Wang, Bin Jie Xiao, Sun Fang Dai
• Keywords: Histogram Concavity Analysis, Lane Detection, Least Square Fitting

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