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Open AccessArticle

Lane Marking Detection and Reconstruction with Line-Scan Imaging Data

by Lin Li 1,*, Wenting Luo 1,* and Kelvin C.P. Wang 2
School of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
School of Civil and Environmental Engineering, Oklahoma State University, Stillwater, OK 74078, USA
Authors to whom correspondence should be addressed.
Sensors 2018, 18(5), 1635;
Received: 15 April 2018 / Revised: 10 May 2018 / Accepted: 17 May 2018 / Published: 20 May 2018
(This article belongs to the Special Issue Visual Sensors)
Abstract: Lane marking detection and localization are crucial for autonomous driving and lane-based pavement surveys. Numerous studies have been done to detect and locate lane markings with the purpose of advanced driver assistance systems, in which image data are usually captured by vision-based cameras. However, a limited number of studies have been done to identify lane markings using high-resolution laser images for road condition evaluation. In this study, the laser images are acquired with a digital highway data vehicle (DHDV). Subsequently, a novel methodology is presented for the automated lane marking identification and reconstruction, and is implemented in four phases: (1) binarization of the laser images with a new threshold method (multi-box segmentation based threshold method); (2) determination of candidate lane markings with closing operations and a marching square algorithm; (3) identification of true lane marking by eliminating false positives (FPs) using a linear support vector machine method; and (4) reconstruction of the damaged and dash lane marking segments to form a continuous lane marking based on the geometry features such as adjacent lane marking location and lane width. Finally, a case study is given to validate effects of the novel methodology. The findings indicate the new strategy is robust in image binarization and lane marking localization. This study would be beneficial in road lane-based pavement condition evaluation such as lane-based rutting measurement and crack classification. View Full-Text
Keywords: laser sensor; line scan camera; lane marking detection; support vector machine (SVM); image binarization; lane marking reconstruction laser sensor; line scan camera; lane marking detection; support vector machine (SVM); image binarization; lane marking reconstruction
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Li, L.; Luo, W.; Wang, K.C. Lane Marking Detection and Reconstruction with Line-Scan Imaging Data. Sensors 2018, 18, 1635.

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