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系統識別號 U0026-1108201103232200
論文名稱(中文) 自動化從測量車影像序列獲取交通路標空間資訊之研究
論文名稱(英文) Automatic Acquisition of Spatial Information of Traffic Signs from MMS Image Sequences
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系碩博士班
系所名稱(英) Department of Geomatics
學年度 99
學期 2
出版年 100
研究生(中文) 任志恒
研究生(英文) Chi-Hang Iam
學號 p6697108
學位類別 碩士
語文別 中文
論文頁數 76頁
口試委員 指導教授-曾義星
口試委員-陳良健
口試委員-史天元
中文關鍵字 測量車影像序列  路標偵測  約制罩窗  路標辨識 
英文關鍵字 image sequence  traffic sign detection  restricting mark  traffic sign recognition 
學科別分類
中文摘要 交通路標在日常生活中佔有相當高的重要性,若能收集路標的空間資訊並加以管理,將有助於都市規劃及汽車導航統系統的設計。以往,人們若要收集或更新路標的空間資訊,主要是以外業的方式進行,但交通路標的資料眾多,以外業的方式進行收集,會需要大量的人力資源和時間,其效率並不理想。隨著測量車的發展,其可快速獲取大量具定位資訊的影像序列,若能利用此特性來進行交通路標資訊的收集,將可提高其資料收集的效率。
在測量車進行空間影像序列收集時,常常會受到很多周邊環境因素的影響,例如影像拍攝時,成像光源不足導致影像明亮度過低及對比度不足,還有就是路標周邊的背景的顏色跟路標的顏色相似,讓交通路標被背景所混淆或路標與背景相連,這些原因都會造成路標偵測和路標辨識的困難和失敗。因此,本研究利用調整路標偵測罩窗的方法,改變罩窗的尺寸來偵測被混淆的路標,另外還有利用直方圖等化的方法來降低成像光源不足的問題。
本研究利用兩組品質不同的影像序列進行測試,從實驗成果顯示,對於品質較好的仿測量車影像,本研究所提出的方法,在路標偵測和路標辨識精度上都能達到88%以上,而整體也有84%,效果相當理想。
而另一組影像是本系所研發的測量車所拍的影像序列,偵測和辨識也能有80%以上的精度,而整體精度雖然稍為低一點點,只有73%左右,但在測試影像所拍到的真實世界上的路標中,有將近90%的真實路標有在影像上被偵測出來,並且辦識正確。
從以上實驗顯示,本研究所提出的方法能有效地偵測及辨識出大部份現實空間中的路標。若從辨識到的路標中,找出各對應的共軛路標影像,再導入測量車的定位資訊,就能把交通路標的位置定位出來。
英文摘要 Traffic signs play an important role in our daily life. If the spatial information of traffic signs are collected and managed, it will give a great benefit to the department of urban planning or car navigation system. In the past, the method to collect traffic signs is site investigation. However it is inefficient and labor-intensive because the large number of signs. With the development of Mobile Mapping System(MMS) which can obtain the image sequences with location information, the collection of traffic signs expected to be done more efficiently by using MMS.
The quality of sequence images for sign recognition suffers from many environment factors. For example, the brightness, the contrast, the homogeneity of colors between traffic signs and non-traffic signs. These factors will cause the sign detection and recognition very difficult. For this reason, in this research, we ease the difficulties by adjusting the scale of the constraint windows to detect the signs and, using histogram equalization to improve the brightness and contrast of the image sequences.
Two image sequence data with different quality are used for testing. The first image sequence is captured to simulate the MMS image and the quality is better than the real MMS image sequence. By applying our developed recognition method on the sequence, the accuracy of signs detection and recognition both can be higher than 88%. The overall accuracy is 84%.
The other image sequences are the real MMS images, the accuracy of sign detection and recognition also approaches higher than 80%. The accuracy of the object base can achieve 89%, although the overall accuracy of the image base is just 72%.
From the results, the most traffic signs in the real world can be detected and recognized. Once the location of traffic sign is known, the position of the traffic signs in the real world can be acquired by applying forward intersection on the stereo images.
論文目次 摘要 I
Abstract II
致謝 III
目錄 V
圖目錄 VIII
表目錄 XI
第一章 緒論 1
1-1 研究背景 1
1-2 研究動機與目的 3
1-3 研究問題設定 5
1-4 相關研究 7
1-5 研究方法 9
1-6 論文架構 10
第二章 路標偵測 12
2-1 偵測流程 12
2-2 色彩模型 13
2-2-1 RGB色彩模型 14
2-2-2 HSV色彩模型 15
2-2-3 色彩轉換 16
2-3 色彩量化 17
2-4 影像分割 19
2-4-1 區域成長法 19
2-5 罩窗約制 21
2-5-1 罩窗設定 22
2-5-2 路標偵測之約制條件 26
第三章 路標辨識 32
3-1 路標辦識流程 32
3-2 影像區塊塊比例尺調整 34
3-2-1 雙立方內插法 34
3-3 路標圖樣識別 35
3-3-1 標準化相關係數法 36
3-3-2 識別機制 37
3-4 路標辨識 39
3-4-1 辨識策略 39
3-4-2 “禁止進入”路標之誤判消除 40
第四章 實驗成果與分析 41
4-1 模擬測量車影像實驗 41
4-1-1 路標偵測成果 44
4-1-2 路標辦識成果 48
4-2 測繪車影像資實驗 51
4-2-1 路標偵測成果 54
4-2-2 路標辨識成果 58
第五章 結論與未來研究 69
參考文獻 73
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