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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/85083

    Title: 應用深度學習演算法萃取移動式測繪系統影像之道路標線;Deep Learning for Extracting Road Markings from Mobile Mapping System Images
    Authors: 張致維;Chang, Chih-Wei
    Contributors: 遙測科技碩士學位學程
    Keywords: 深度學習;移動式測繪系統;道路標線辨識;高精地圖;Deep Learning;Mobile Mapping System (MMS);Lane Marking Recognition;HD Map
    Date: 2020-12-24
    Issue Date: 2021-03-18 17:36:53 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 隨著電腦運算能力的進步與空間資訊領域的發展,政府與企業相繼投入自動駕駛系統的相關研究及開發。自動駕駛車(以下簡稱自駕車)技術往往仰賴多種車載感測器進行即時運算,然而若能利用預先建置的高精地圖提供自駕車周遭環境的空間資訊作為定位定向輔助,便能大幅減低車載電腦的運算負擔。因此,如何高效率地產製高精地圖的向量圖資是開發自駕車應用中至關重要的一個環節。本研究之目的為應用開源測繪車影像資料集與深度學習演算法,萃取移動式測繪系統影像中的道路標線,自動化地產製可供高精地圖使用的向量圖資。
    ;With the growing computing capacity and the development in the field of Geoinformation, governments and enterprises have actively invested in the research and development of autonomous driving systems. Most autonomous driving technologies rely on multiple on-board sensors for real-time operation. However, the implementation of pre-built High-Definition map (HD Map) can provide detail information of the surrounding environment for self-driving cars. For instance, positioning and orientation, which can significantly reduce the computational burden of on-board computers. Therefore, efficiently producing data layers for HD maps is an important step in the development of self-driving car technology. The purpose of this research is to apply the open source street scene image dataset and deep learning algorithms to extract lane markings from the Mobile Mapping System (MMS) images, and to automatically create the data layer for HD maps.
    The proposed scheme consists of three parts: (1) semantic segmentation, (2) direct georeferencing, (3) lane marking correction. In the semantic segmentation part, this study uses the open source self-driving car image dataset ApolloScape to train the deep learning model. In order to increase the accuracy of image segmentation, this study crop the training images and remove the surveying car-roof area which is not shown in the test data. Also, it removes parts of training images with high illumination and noise. Afterward, in order to get the coordinates of lane markings, this study uses photogrammetry method and orientation parameters of camera to derived the positional information of the lane markings. Finally, produce the HD Map data layer according to the regulations issued by Ministry of Transportation and Communications, Taiwan.
    The experimental results show that the proposed method can effectively identify the road markings in the MMS images. In addition, the developed method can further correct the mis-classified and missing parts in the subsequent lane marking correction steps, and produce reliable road marking data layer for High -Definition Map.
    Appears in Collections:[遙測科技碩士學位學程] 博碩士論文

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