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    题名: 嵌入式道路速限標誌偵測與辨識系統設計與實作
    其它题名: Design and implement of an embedded road-speed-sign detection and recognition system
    作者: 廖憲琛;Liao, Hsien-Chen
    贡献者: 淡江大學電機工程學系碩士在職專班
    蔡奇謚;Tsai, Chi-Yi
    关键词: 速限號誌辨識;嵌入式系統;影像矩;支持向量機;特徵描述提取;Road-Speed-Sign recognition;embedded system;image moments;support vector machine;feature descriptor extraction
    日期: 2015
    上传时间: 2016-01-22 15:06:26 (UTC+8)
    摘要: 由於現今車載系統中,針對道路行車速限的提示,一般方法常使用 GPS 定位資訊搭配事前建立的資料庫,搜尋該路段行車速限紀錄,以提示駕駛注意行車的速限,但此方法往往因資料過舊或道路的臨時變更,而導致錯誤的提示。本文提出一個能偵測與辨識道路速限標誌的方法,並且實作出一套視覺行車輔助系統,其為結合行車記錄器所拍攝之影像,在車輛行進間,即時的辨識出道路速限標誌之道路速限。針對不同角度及距離所偵測到的道路速限標誌,為提高此行車輔助系統的分類辨識成功率,本文亦提出一種特徵描述的方法流程,其結合影像質心和輪廓距離角度的特徵計算方法,能在有限的角度範圍內,達到平移不變、尺度不變、旋轉不變的特徵描述方式,作為支持向量機的分類的數據資料。接著透過大量資料庫的建立,讓支持向量機分類的能力提高。最後,將訓練好的分類器移植至Radxa Rock Pro 嵌入式平台上,其配備 ARM Cortex-A9 1.6GHz 四核心的處理器,可運行 Android 4.4.2 的作業系統,並移植OpenCV 和 LibSVM 的函式庫,在 JNI 層實作本論文所提出的影像辨識系統,對嵌入式平台效能做程式碼的優化,以達到即時偵測與辨識出道路的速限之目的。
    In a vehicle navigation system, the design of road-speed-limit warning function is often implemented by using global positioning system combined with a pre-established traffic sign database to search the recorded road-speed-limit sign close to the current location. However, this method may obtain a wrong advice result when the database does not update to the latest version. This thesis addresses the design and implementation of a vision-based driver-assistance system based on an image-based road-speed-limit sign recognition algorithm, which is able to automatically detect and recognize the road-speed-limit sign on the road in real-time. To improve the recognition rate of road-speed-limit signs with different view angles and scales, this thesis also presents a new feature extraction algorithm, which describes an object using the distances between the object’s image centroid and its contour under some specific angle conditions. The proposed feature descriptor achieves translation, scale, and rotation invariant with respect to a certain view angle range. This property helps to improve the recognition accuracy of a support vector machine classifier trained by using a large database of traffic signs. The proposed vision-based driver-assistance system had been implemented on a Radxa Rock Pro embedded platform equipped with an ARM Cortex-A9 Quad Core 1.6GHz CPU running Android 4.4.2 operating system. The proposed system was implemented in JNI layer using OpenCV and LibSVM libraries, allowing achieving real-time performance by C code optimization.
    显示于类别:[電機工程學系暨研究所] 學位論文

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