Lane marking detection based on a mono camera sensor provides a low cost solution to both ITS (intelligent transportation systems) and DAS (driver assistant systems). A number of methods and implementations have been reported in the literature. However, reliable detection is still an issue. Traditional approaches are mostly based on statistics or Hough transforms. However, the former approaches usually include many irrelevant detection areas, and the latter are not practical because actual lanes are not usually suitable for the approximation with linear or polynomial equations. In this paper, we focus on increasing the reliability of detection by reducing the detection of irrelevant areas while improving the detection of actual lane marking areas, which is usually a tradeoff for most conventional approaches. We use scale-space for that. Through experiments with real images obtained from various environments, we could achieve a significant improvement over traditional approaches.