道路標線係組成道路要件之一，具備解釋道路交通指引與規範之功能，道路標線於經年累月使用下，導致其外觀磨損而影響功能。現係由巡檢人員依照規章制度至現地勘查，以巡檢人員自主判斷道路標線磨損情形，紀錄對應道路標線之磨損程度。
磨損程度除了由巡檢人員判斷與紀錄之外，亦有相關研究提出採用移動式測繪系統自動化計算磨損程度，因近年影像偵測標線方法準確度提升，故可透過影像偵測道路標線，並進一步計算其磨損程度。然而，此方法受到道路標線磨損條件影響，如若道路標線已完全磨損至無法透過人眼辨認位置，則透過影像亦難以偵測得出其位置與面積，使得計算磨損程度成果受限。
為改善磨損條件影響致使影像偵測與磨損程度計算成果限制、本研究結合高精地圖與移動式測繪系統發展自動化計算磨損程度方法，透過高精地圖提供道路標線物空間坐標系之位置，投影計算道路標線於影像坐標系之範圍進行偵測，並利用投影正射影像計算標線面積與磨損程度，改善影像受標線嚴重磨損影響而難以偵測之限制，並數據化道路標線磨損程度資訊。
本研究依道路標線種類與對應色彩區分道路標線為白色、黃色與紅色，採用各色彩具備之特徵偵測其位置與面積。經統計各場域之研究成果，計算得各類別道路標線磨損程度準確度kappa value為白色0.62、紅色0.59與黃色0.54，各色彩種類道路標線成果皆包含0%至100%等不同磨損程度案例。基於研究成果得知本研究之可行性，得以計算不同磨損程度之道路標線，以及改善嚴重磨損標線計算磨損程度之限制。

The purpose of this study is to overcome the limitation of image detection in properly assessing the condition owing to fully deteriorated lane markings and accomplish lane markings condition digitization. This study proposes an algorithm for automatically assessing the conditions of lane markings that integrates HD Map(High Definition Map) with MMS(Mobile Mapping System). Generating a detecting ROI(Range of Interest) by projecting the HD Map 3D coordinate system location onto the image’s coordinate system. This study applies the color feature to recognize lane markings, which can be classified into three categories: white, yellow and red. Following the detection of lane markings in images, this study computes the area and condition of lane markings using orthorectified images. According to statistical results of study area, the kappa values for assessing the accuracy of conditions of lane markings in each category is 0.62 for white, 0.54 for yellow, and 0.59 for red. In conclusion, this study improves the limitation of assessment conditions of lane markings with image detection result. Achieving the purpose of automatically calculating and digitizing the conditions of lane markings through integration of HD Map and MMS.

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