基於色彩分離與邊界強化的車道線偵測

Lane Line Detection Based On Color Separation and Boundary Enhancement

吳祐鴻(You-Hong Wu)

指導教授 ： 張恆華

國立臺灣大學/工學院/工程科學及海洋工程學系/碩士(2023年)

https://doi.org/10.6342/NTU202303260

摘要

交通安全是生活中關於人身安全的重要議題，隨著近年來交通議題在社會上愈來愈受重視，這也帶動了自駕車及車輛輔助駕駛系統的發展。基於影像處理的車道線偵測系統在自駕車中愈來愈受重視。為了解決車道線檢測系統在行駛中，容易受到附近行進中的車輛影響而檢測錯誤的情況，本論文提出了一種基於影像處理的多車道線偵測方法。首先使用高效率的YOLOP模型過濾掉影像中的車子，接著使用全域的二值化方法找出白色和黃色的車道線，再使用快速的邊緣偵測方法擷取車道線特徵。經由逆透視轉換後，我們使用k-Means來將擷取到的邊緣特徵分類成個別的車道線，最後根據各個車道線類別用拋物線函數來擬合車道線。我們的方法在TuSimple資料集中平均Accuracy、Precision和Recall能達到0.727、0.509和0.407。

關鍵字

車道線偵測 ； YOLO ； 邊緣偵測 ； 閥值化 ； 聚類分析

並列摘要

Traffic safety is an important topic regarding personal safety in life. With the increasing attention to traffic issues in society in recent years, there is a need for the development of self-driving cars and vehicle driver assistance systems. Image-based lane line detection systems are essential for self-driving cars. In order to prevent the lane line detection from being easily affected by nearby moving vehicles and obstacles during driving, this thesis proposes a multi-lane line detection method based on a series of image processing steps. First, an efficient YOLOP model is used to filter out the cars in the image, and a global thresholding method is used to locate the white and yellow lane lines. Then a fast edge detection method is used to extract the lane line features. After applying the inverse perspective mapping, we employ k-Means to classify the extracted edge features into individual lane lines. Finally, according to each lane line category, a parabolic function is used to fit the lane line. The average Accuracy on the TuSimple dataset reached 0.727, with the Precision of 0.509 and the Recall of 0.407.

並列關鍵字

lane line detection ； YOLO ； edge detection ； thresholding ； cluster analysis

參考文獻

[1] 中華民國交通部. 近五年交通事故統計. 中華民國政府, 18(2):203–211, 2021.

[2] Huifeng Wang, Yunfei Wang, Xiangmo Zhao, Guiping Wang, He Huang, and Jiajia Zhang. Lane detection of curving road for structural highway with straight-curve model on vision. IEEE Transactions on Vehicular Technology, 68(6):5321–5330,2019.

[3] Bandarage Shehani Sanketha Rathnayake and Lochandaka Ranathunga. Lane detection and prediction under hazy situations for autonomous vehicle navigation. In 2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer), pages 99–106, 2018.

[4] Ravi Kumar Satzoda, Suchitra Sathyanarayana, Thambipillai Srikanthan, and Supriya Sathyanarayana. Hierarchical additive hough transform for lane detection. IEEE Embedded Systems Letters, 2(2):23–26, 2010.

[5] Umar Ozgunalp, Rui Fan, Xiao Ai, and Naim Dahnoun. Multiple lane detection algorithm based on novel dense vanishing point estimation. IEEE Transactions on Intelligent Transportation Systems, 18(3):621–632, 2017.