文章基本信息

标题：A Model for Segmentation and Distress Statistic of Massive Pavement Images Based on Multi-Scale Strategies
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作者：LI Qingquan ; LIU Xianglong
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B5
页码：63-68
出版社：Copernicus Publications
摘要：Conventional distress detection method which dealing with each image through single algorithm and under single scale with lower efficiency. A robust and high-efficiency model for segmentation and distress statistic of massive pavement images which based on multi-scale space is proposed in this paper. It based on the facts that: (1) the crack pixels in pavement images are darker than their surroundings and continuous; (2) images associated with the same road section with the consistence of pavement texture structure. The proposed model contains three stages mainly: Image segmentation is implemented based on neighboring difference histogram method, then the weighted multi-scale based distress statistical is executed to get the crack index of the pavement images, in the end it separates the cracked images from massive images through the distribution of the crack index and achieved the objective of improving detection efficiency. Experiments results demonstrated that the proposed method can pick up the cracked images from massive pavement images correctly and effectively, and the time consuming is less than one third of the classical flow while the missing detection rate not exceed five percent
关键词：Image Processing; Recognition; Image Understanding; Feature Detection; Location Based Sevices; Industrial ; Measurement