15 November 2017 An improved self-correct algorithm for pavement texture
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Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 106054E (2017) https://doi.org/10.1117/12.2296352
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China
Abstract
Pavement texture has great influence in terms of road safety. Until recently, laser distance measuring technique that can measure pavement texture depth has become available. Compared with the volumetric patch technique which are now widely used, the laser distance measuring is a relatively new technology. This method has certain applications in the world. Through a large number of experiments, the researchers found that the accuracy of many instruments has not been high enough to fulfill the requirement. Local anomaly is the main factor of the accuracy in the distance measurement. This paper presents an improved self-correct algorithm for texture depth. The objective is to analyze the improved self-correct algorithm used in vehicle bearing road laser texture-meter for pavement texture depth evaluation carried out under ordinary testing conditions, referring to the Chinese standards in pavement texture depth. All pavement texture measurements were performed on four selected road pavements with different texture depth. The novel approach obtained a complete and consistent three-dimensional model representation from road surface scans, using three-dimensional line-scan technology. The four selected road pavements measured with 100 vehicle bearing road laser texture-meters respectively. The improved self-correct algorithm was applied to a vehicle bearing road laser texture-meter. The improved self-correct algorithm reduced the indication error of the general algorithm. The manufacturers can adjust the parameters according to the result, so that it can improve the reliability of the instruments.
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Huayang He, Guangwu Dou, Jinning Zhang, "An improved self-correct algorithm for pavement texture", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106054E (15 November 2017); doi: 10.1117/12.2296352; https://doi.org/10.1117/12.2296352
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