# 臺灣博碩士論文加值系統

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 本文就嵌入式平台車道偵測系統面臨之問題:ARM(PXA255)在硬體上不支援除法指令和浮點數運算，以嵌入式系統實驗室發展之車道偵測演算法為例，提出一些程式設計優化的方法:盡量減少除法及浮點數的運算。演算法處理的流程為︰載入影像提高對比度二值化車輛頂點搜尋車道特徵點搜尋可能線段連結顯示結果。首先，針對提高對比度副程式中，利用預先建表方式將參考文獻[28]之程式需要執行25600次的除法運算減少到只需執行256次。其次是分別在二值化及車輛頂點搜尋副程式，利用技巧將原本需要做乘法及加法的運算，簡化成只做加法運算即可。在車道特徵點搜尋副程式中，透過資料分析於參考文獻[28]中將需要比對256*100次減少成256*5次，最後在可能線段連結副程式中，評估資料大小的範圍後將原本使用浮點運算改成整數運算，可大幅減少運算時間。在未優化之車道演算法中平台處理時間約為47.5ms/frame，然而經由本文避免除法運算優化及浮點數優化策略，實驗結果顯示優化後車道偵測演算法整體處理時間為28.9ms/frame，優化後處理速度可提升39.28%。
 In this thesis, we propose some techniques to overcome the non-supporting division instruction and floating operation problems in an ARM embedded platform (PXA255) for application in the lane detection system. The strategy is to minimize the division instruction and floating operation in the program design. The processing flow of our lane detection algorithms describes as followed: load image  contrast enhancement  binary  find peak points  find edge points  edge connection  line segment combination display result. Firstly, we build a lookup table to reduce the division operation from 25600 to 256 times in the subprogram of contrast enhancement. Secondly, the operation of multiplication and addition was reduced to only addition operation by a predefined address technique in the subprogram of binary and find peak point. Thirdly, in the subprogram of find edge point, we analyze the data group property to reduce the data comparison from 256*100 to 256*5 times. Finally, in the subprogram of edge connection, we use the integer operation to substitute the float-point operation without loss the accuracy. It can greatly reduce the processing time. The lane detection process time is about 47.5 ms/frame before program design optimization. After optimization, the lane detection process time is about 28.9 ms/frame. The enhancement of processing speed is as high as 39%.
 目錄指導教授推薦書 i口試委員審書 ii長庚大學博碩士論文著作授權書 iii致謝 iv中文摘要 vABSTRACT vi目錄 vii表目錄 ix第一章 緒論 11.1 前言 11.2 文獻回顧 2第二章 影像前處理 72.1 影像處理系統的硬體組成 72.2 數位影像的表示法 92.3 何謂清晰的影像 92.4 增加影像對比度 112.5 二值化 142.6 動態調整二值化區域 16第三章 車道線偵測演算法 193.1 車輛偵測系統架構[24] 193.2 車道特徵點擷取[24] 213.3 車道線段偵測[24] 243.4 車道標線特徵點聚集[24] 253.5 車道線段接合[24] 293.6 車道標線邊界篩選[24] 323.7 車道接合與選取[24] 343.8 減少資料量[23] 35第四章 實驗平台、分析與結果 364.1 平台簡介[28] 364.2 車道辨識系統之背景[28] 394.3 平台問題[28] 424.4 實驗設計[28] 454.5 挑戰新平台 494.6 優化車道偵測辨識演算法[28] 51 4.6.1 優化強化對比[28] 53 4.6.2 優化二值化[28] 58 4.6.3 優化頂點搜尋[28] 60 4.6.4 優化特徵點聚集[28] 62 4.6.5 優化線段連結[28] 68 4.6.6 優化前後測試結果[28] 70第五章 結論 725.1 結論 725.2 綜合比較 74參考文獻 76表目錄表1-1 行駛速度下車道辨識所需距離 2表4-1 PXA255硬體規格 37表4-2 PXA255使用的個人電腦規格與編譯環境 39表4-3 History Summary 42表4-4愛星AC-511 Camera規格 45表4-5 SAMSUNG6410L硬體規格 47表4-6 新灰階Table表 55表4-7 Find Edge Point比較表 67表4-8整數與浮點數值域比較表 70表4-9 Optimization Summary 71表5-1效能比較表 75圖目錄圖2-1影像處理裝置的構成 7圖2-2彩色轉灰階 9圖2-3強化對比度示意圖 12圖2-4黃色水平列影像灰階剖面圖之分析 13圖2-5整張影像之灰階分析 14圖2-6二值化示意圖 15圖2-7二值化示意圖 16圖2-8動態二值化示意圖 17圖2-9動態二值化與單一閥值比較圖 18圖3-1車道偵測系統處理流程圖 19圖3-2車量偵測示意圖 20圖3-3車量偵測結果 20圖3-4車道偏移警告與前車距離系統架構圖 21圖3-5 Peak Point之分析 22圖3-6 Peak Point偵測結果 23圖3-7車道線段偵測之結果 25圖3-8(a)11x15鄰接視窗(b)小線段影像 25圖3-9區塊群組演算法示意圖 27圖3-10 Peak-Finding演算法流程圖 28圖3-11以金字搭表示各運算所佔比率 29圖3-12線段連結示意圖 29圖3-13線段連結示意圖 32圖3-14 中華民國車道標線示意圖 33圖3-15車道邊界篩選的結果 34圖3-16車道接合與選取結果 34圖3-17減少資料量示意圖 35圖4-1 XBase PXA255架構圖 37圖4-2 LDWS Background示意圖 40圖4-3 Find Peak Point 41圖4-4 PXA255開發板實現即時車道偵測辨識系統示意圖 43圖4-5 USB2.0即時車道偵測辨識系統 46圖4-6 SAMSUNG610L Challenge示意圖 49圖4-7 車道偵測辨識演法示意圖 52圖4-8 車道偵測辨識演法示意圖 52圖4-9 新灰階值曲線圖 55圖4-10 Original Contrast Enhancement Flow Chart 56圖4-11 Optimized Contrast Enhancement Flow Chart 56圖4-12車道圖像 57圖 4-13 Contrast Enhancement (Original vs. Optimized) 57圖 4-14 Binary示意圖 58圖 4-15 Binary流程圖 59圖 4-16 Binary示意圖 59圖 4-17車道圖像 60圖 4-18 Binary (Original vs. Optimied) 60圖 4-19 車道圖像 61圖 4-20 Find Peak Point (Original vs. Optimied) 61圖 4-21 Find Edge Point示意圖 62圖 4-22 Find Edge Point示意圖 63圖 4-23 Find Edge Point示意圖 64圖 4-24 圖4-24(a)~4-24(j) 64圖 4-25 車道圖像 67圖 4-26 Find Edge Point (Original vs. Optimied) 67圖 4-27 車道圖像 69圖 4-28 Edge Connecting (Original vs. Optimied) 69
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 1 以嵌入式平台實現車道偵測系統

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