影像道路線偵測，藉由從複雜的影像中準確的找出路面上左右兩條道路線，對於目前所推行的智慧型車輛有顯著的貢獻。常見的車輛安全系統有道路偏離、行人偵測、前車距離偵測、障礙物偵測等。上述這些狀況均發生在車輛所行駛的路線範圍中，由此可見車道線偵測扮演重要的角色。一般來說，車輛會行駛在不同的環境中，例如:下雨天、陰天、晚上亦或是起霧的狀況，系統要能在各種狀況下準確的運作，否則就沒有實際應用的價值了。為了在任何狀況下都能夠準確的運作，本論文提出了一種區域自適性演算法來分析影像子區塊的變化，演算法能自行適應環境光的改變調整其參數，有別於以往對整張影像以同樣的方法做處理，子區塊分析能夠適應同一張影像每個區塊有不同光影變化的場景。不僅如此，若在區塊中有找到道路線的蹤影，我們會利用這些區塊做自適性的ROI(region of interest)來降低運算時間與複雜度。根據我們實驗的結果證明了提出的演算法能有效降低運算複雜度，並保持良好的偵測效率。

Lane-mark detection is one of the most important parts in intelligent transportation systems (ITS). We use the camera mounted front of vehicle to capture the road scene for detecting lane-marks. The proposed methods consist of six parts. In the first part, we determine the region of interest (ROI) of captured image and apply the Canny edge detector to investigate boundaries. In the second part, we divide the boundary image into sub-images to calculate local edge-orientation of each block and remove the edge with abnormal orientation. In the third part, we propose the edge-pair scanning method to verify the edges which belong to lane-marks by using the relationship of adjacent edges of lane-marks and the width between these two edges. In the fourth part, we also divide the image into sub-images and apply the feature that road lane-marks are always painted with high contrast colors with the road surface. Then, we use multi-adaptive thresholding method for each block. In the fifth part, we develop the mechanisms of verification and treatments for these candidate lane-mark edges. In the sixth part, we calculate the lane-marks as straight line and curve line models and apply Kalman filter to track it. In the experiment, the proposed system is evaluated in several situations such as bad weather conditions, shadow effect, or road sign on the road. The results show that the proposed method can detect the lane-marks in real-time for various different environments.

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