## 1 Introduction
However, GPS is greatly affected by satellite signal intensity, and the cost of high-precision maps and LiDAR is high, which limits the large-scale application. Luckily, deep learning technologies have been developed rapidly in recent years and have been applied to the lane detection and tracking field as an extension of the traditional machine learning [21]. [PERSON] [22] raised a lane recognition algorithm that sufficiently extracts features of road surface by convolution neural network (CNN) and recurrent neural network (RNN). [PERSON] [23] presented an algorithm for image improvement based on CNN, and completed lane detection with the RANSAC. [PERSON] [24] proposed a traffic scene segmentation algorithm on the basis of CNN to enhance the accuracy of lane detection. Although satisfactory results in lane detection can be achieved, post-process is still needed to locate the position of vehicle between two lane boundaries. Therefore, this study proposes a CNN-based recognition model that can obtain the lateral distance between vehicle and lane strip directly from the input image without pre-processing and post-processing. However, the training of the high-accuracy CNN model usually requires a lot of label images, and building a ground-truth database is complex and time-consuming [25]. Currently, the commonly used method is to mark the lane position in every frame of a video, and the processing of each frame takes about one minute, that is to say, it would take about one month to label a one-hour video [26]. Hence, a more advanced algorithm with higher efficiency of label images generation is needed. [PERSON] [27] proposed a ground truth generation algorithm that is based on time-slice, wherein every frame needs only two labelled points and the ground truth can be obtained by stacking the frames from the bottom to the top. In order to speed up the labelling process, [PERSON] [28] proposed an improved time-slice algorithm, where lane pixels were located quickly with the help of Canny. With this approach, the efficiency of manual labelling was improved greatly, but it still depends on manual annotation. Since the generation of the label data requires a large number of images and manual annotation, which is cumbersome and time-consuming, and accuracy of the label is greatly affected by human factors, some alternative algorithms for label image generation were studied and some trained model has achieved promising results. [PERSON] [29] developed a lane detection algorithm to achieve a centimeter-level recognition accuracy using 2500 synthetic images on the SIVIC platform. [PERSON] [30] trained a deep learning model for lane detection using 80,000 real scene images and 40,000 synthetic images, and the accuracy of detection reached 94%. Using these image synthetic algorithms, the label images can be obtained on demand, and for this reason, the efficiency of model training can be improved greatly. Nevertheless, the speed of image synthesis is usually unsatisfactory. Meanwhile, the condition of light, road surface wear and pollution on the road are not taken into consideration. Thus, a new algorithm for synthesizing label images with factors of real traffic scene is needed. Among the many existing image synthesis algorithms, Image Quilting [31] is the most commonly used. The texture information on the original image can be well preserved. Due to the obvious advantage and spotlight of Image Quilting, the algorithm has attracted much attention from researchers. [PERSON] [32] proposed an improved Image Quilting algorithm that is based on the Graph Cut and solved the problem of boundary distortion on the basis of graph theory. [PERSON] [33] introduced the Genetic Algorithm to search the nearest neighbor in an adaptive way to match the texture block, with the efficiency being significantly improved. [PERSON] [34], by taking the Partial Non-Scalar Distance as a minimum path metric, eliminated the seam, and obtained the synthetic images with high quality.