Lane-mark extraction for automobiles under complex conditions

Lane-mark detection is one of the most important parts of the intelligent transportation system (ITS). Cameras mounted in front of automobiles are used to capture the road scene to detect lane-marks. This paper proposes a new lane-mark extraction algorithm that consists of six parts. We first determine the regions of interest (ROIs) of captured images and investigate their boundaries. Then, we divide the boundary image into subimages to calculate the local edge-orientation of each block and remove edges with abnormal orientations. After that, the multi-adaptive thresholding method is employed for each subimage so that the system can work efficiently for an image that has different contrasts to indicate sub-regions for lane-marks. We then verify these candidate lane-mark edges and fit the lane-marks using either straight or curved line models. A Kalman filter is also applied to track them. The proposed method is evaluated in several situations, including in bad weather conditions and in the presence of shadow effects, obstacles such as wipers, or road signs on the road. The results show that the proposed method can detect the lane-marks in real time for various complex environments.