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研究生:陳建榕
研究生(外文):Chen, Jerome Jianrong
論文名稱:針對槽化線之車道線檢測
論文名稱(外文):Lane Detection for Channelizing Lines
指導教授:王傑智
指導教授(外文):Wang, Chieh-Chih
口試委員:林文杰邱維辰林惠勇
口試委員(外文):Lin, Wen-ChiehChiu, Wei-ChenLin, Huei-Yung
口試日期:2019-04-11
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電控工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:英文
論文頁數:62
中文關鍵詞:車道線檢測槽化線
外文關鍵詞:Lane DetectionChannelizing Lines
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⾞道線檢測在無⼈⾞駕駛的系統中扮演著相當重要的⾓⾊。在本⽂中,
我們發現當⾞⼦進⼊⾼速公路閘道出⼊⼝時,過去的⽅法常受槽化線區域
影響,無法完整的描述槽化線左右兩側的⾞道線。因此本⽂針對⾞道線檢
測中,容易被當成錯誤或未被檢測的區域,槽化線,提出⼀套解決⽅案。
我們不僅針對⾼速公路上閘道出⼊⼝固定出現的雪弗⿓型槽化線區,亦
能處理在都市中的斜型槽化線區。本⽂的最後,提出了有別於過往的資料
集,富含槽化線區域的實驗資料及實驗結果,平均準確率達91.11%。
In this thesis we consider one of challenge cases in a lane detection system, which is channelizing line, and propose an efficient method to not only keep the pixels belonging to the lane located on the channelizing line, but also detect and model the lanes which were filtered from the observation. Channelizing lines are used to guide drivers where travel in the same direction for permitting on both sides, such as entrance and exit ramps. Channelizing line usually has same intensity with the lane, but has pavement markings, solid white lines with wide diagonal lines or chevrons within two channelizing lines, which are usually influence the detection result in several previous work. The experiments show that the proposal has a good performance even passing through several channelizing lines. While passing through single channelizing line zone, the average accuracy can reach to 91.11%. Experiments show that our approach reaches competitive performances on channelizing lines.
1 Introduction 1
2 Related Work 4
3 Approach 8
3.1 Lane Marking Segmentation . . . . . . . . . . . . . . . . . . . 10
3.1.1 Inverse Perspective Mapping . . . . . . . . . . . . . . . 11
3.1.2 Separable Gaussian Filter . . . . . . . . . . . . . . . . 12
3.1.3 Histogram Equalization & Intensity-based Segmentation 13
3.2 Lane Marking Detection . . . . . . . . . . . . . . . . . . . . . 16
3.2.1 Lane Marking Extraction . . . . . . . . . . . . . . . . 17
3.2.2 Lane Marking Clustering . . . . . . . . . . . . . . . . . 18
3.2.3 Lane Modeling . . . . . . . . . . . . . . . . . . . . . . 22
4 Experiment 24
4.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.1.1 NCTU Dataset . . . . . . . . . . . . . . . . . . . . . . 26
4.1.2 TuSimple Dataset . . . . . . . . . . . . . . . . . . . . 27
4.2 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.2.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.3 Failure cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5 Conclusion 58
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