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In this study, a system for detecting stop lines on roads with damaged paint is developed to enhance a digital map localization system. Existing methods to detect stop lines focus on features such as straight edges and adequate size; however, these methods are not suitable to be used in rural areas because the paint of stop lines on the roads is damaged sometimes. In addition, lane marks, which are focused on by other existing methods, are often not present on actual roads in rural areas. Thus, to enable the detection of stop lines in the absence of conditions necessary for using the abovementioned features, we focus on pieces of faint features of damaged stop lines. First, we extract the positive and negative edges from an inverse perspective mapped image of the camera input by using a Sobel filter. Next, we verify the pairs of positive and negative edges from the trinarized edge image by confirming the width between both edges. Subsequently, we detect the candidates of stop lines by analyzing the distribution of the line segments extracted by the Hough transformation. In addition, we combine the data of the estimated driving distance and the result of detection of the preceding vehicles with the proposed system to prevent false detections in terms of bicycle crossing lanes and preceding vehicles. The damaged stop lines are detected eventually using these processes. To evaluate the performance of the proposed method, we collect driving data on actual public roads. The results of offline evaluations confirm that the proposed system can detect all target stop lines without any false detections, at a reasonable speed. The findings of this study are expected to contribute to the realization of intelligent vehicles on community roads.

Detection of Damaged Stop Lines on Public Roads by Focusing on Piece Distribution of Paired Edges