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    Please use this identifier to cite or link to this item: http://ccur.lib.ccu.edu.tw/handle/A095B0000Q/354


    Title: 利用路面辨識技術於路徑推薦系統之設計研究;Design of Road Surface Detection and Recognition Technology in Route Recommendation and Navigation Preview System
    Authors: 戴俊旻;DAI, JYUN-MIN
    Contributors: 電機工程研究所
    Keywords: 深度學習;預覽系統;路面分類;路徑推薦;Preview system;Road surface classification;Route recommendation;Deep learning
    Date: 2017
    Issue Date: 2019-07-17
    Publisher: 電機工程研究所
    Abstract: 近幾年,使用導航系統至目的地的使用者已日趨成長,但大多數的導航系統是使用最短路徑的方式作為其推薦路徑的選擇。若是使用最短路徑的方式,大多會遇到以下的狀況:(1)車子行駛至狹窄的巷道時,造成與對向來車會車時的不便,以及駕駛必須更小心翼翼的注意周遭的環境。(2)車子行駛至路面狀況不佳之區域或是以石磚所鋪設的道路區域時,駕駛通常需要放慢車速,由於石磚所鋪設的道路區域大多為觀光區域,因此人潮與車潮較多,並且石磚所鋪設的路面在雨天行駛時較柏油所鋪設的路面更容易產生偏滑,以及行駛於路面狀況不好之路段或是以石磚所鋪設的路段時會使駕駛者感受到因路面所產生的顛簸感,造成行車品質不佳。為了盡量避免以上所述的情況產生,本論文提出一種基於街景影像之路徑推薦導覽系統的框架設計。此方法由三大部分組合而成:(1)資料收收集,透過GUI進行路徑的選擇,並下載該條路徑之所有街景影像。(2)路面提取,根據影像計算其消失點,並且以超像素層級的方式利用區域成長分割出路面區域,最後計算該區域的路寬。(3)路面辨識,根據預先收集之石磚路面與柏油路面之類別進行訓練,最後透過訓練完的模組辨識每張影像的路面類別為何。在實驗結果方面,我們針對本文所使用之路面提取的方法以及路面辨識的方法,將提取的結果和分類的結果分別顯示並說明其配分的規則,接著我們將會針對路面分類之準確度進行討論。由於我們透過自定的給分規則計算出各條路徑的推薦分數,因此最後將會檢視我們所提出之推薦系統所推薦出來的路徑之結果是否能有效的解決以上所述的問題。
    In recent years, even though the average using a navigation system for destination user has gradually increased, we continue to witness on news a slight increase in getting lost with a GPS. Most of the getting lost events occur due to the navigation system usually using the shortest paths as their recommended route.Supposedly, we use the shortest path may encounter the following situation:(1)GPS device might lead you down the narrow alley or down a closed road. If we are driving down a narrow alley, we have to pay more attention to our surrounding environment, as well as the inconvenience caused by the vehicle passing cross form the opposite lane. (2)If we are driving down a brick road or driving into some areas of damaged roads, we must lower the speed. The traffic is often dense and crowds because most of brick road area is laid near to the tourist attractions. In addition, the vehicle driving on the brick road areas might more easily skid than driving on the asphalt road areas when it is raining. Furthermore, the driver may feel bumpy when driving pass through some areas of damaged roads or driving on a brick road.To address the above problems, in this thesis, we present a framework of system for vision-based path recommend from street view images.The entire system is a combination of the following three steps:(1)Data collection, the path is selected by graphical user interface. Also, we download the street view images of the whole path.(2)Surface extraction, the vanishing point is calculated from the collection images. The area of the pavement is extracted by the Grow-Cut in super-pixel level, and the road width of the area is calculated finally.(3)Surface classification, training step is carried out on the types of brick pavement and asphalt pavement collected in advance. Finally, the pavement categories for each image were predicted by using our previously trained modules.In the experimental results, in this thesis, we use the surface extraction and classification method, the results of the extraction and classification are displayed, then we explain the rules of its distribution. After that, we will discuss the accuracy of surface classification. Since we calculate the recommend scores for each path through our own scoring rules, we will examine whether the results of the path proposed by our system can effectively solve the above problems.
    Appears in Collections:[電機工程研究所] 學位論文

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