## 3 Experimental Section
### Experimental Dataset  
The dataset used for the experiments includes the CULane [33] dataset and custom dataset with inverse perspective transformation. As shown in Figure 6, the road image is transformed using inverse perspective transform to create a bird's-eye-view road image that reflects the real world. This image shows lane line features more clearly and facilitates feature extraction for the network. In the inverse perspective transformation, the relationship between the original image and the transformed image can be represented by a 3 \(\times\) 3 transformation matrix. This matrix is computed based on four corresponding points in the data image, as shown in this equation:  
\[ \begin{bmatrix}i_{i}x_{i}^{\prime}\\ t_{i}y_{i}^{\prime}\\ t_{i}\end{bmatrix} =map\_matrix\cdot \begin{bmatrix}x_{i}\\ y_{i}\\ 1\end{bmatrix}  \tag{10}\]  
where _map_matrix_ represents the computed \(3\times 3\) inverse perspective transformation matrix, and (\(x_{i}\), \(y_{i}\)) and (\(x_{i}^{\prime}\), \(y_{i}^{\prime}\)) represent the coordinates of the input point and the output point, respectively. The value of \(i\) ranges from 0 to 3, indicating the corresponding four points. \begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Parameter** \\ \hline CPU & Intel(R) Xeon(R) Platinum 8255C CPU @ 2.50 GHz \\ RAM & 45 GB \\ GPU & NVIDIA RTX3090 GPU with 24 GB of VRAM \\ VRAM & 24 GB \\ Programming language & Python 3.7 \\ Deep learning framework and & Pytorch 1.10.2, CUDA 11.6, Cudnn 8.3.2, \\ dependency library & Numpy 1.19.5, tqdm 4.64.1, tensorboard 2.10.1, \\ OpenCV-Python 4.6.0.66 \\ \hline \hline \end{tabular} The hyperparameter settings for the algorithm are presented in Table 3.  
\end{table} 
Table 2: Experimental environment. Figure 6: Inverse perspective transformation: (**a**) original image; (**b**) after perspective transformation. This method allows us to obtain a bird's-eye view of the road without requiring extensive computational resources. It is a simple operation that only requires processing the region of interest (ROI) containing the lane lines, thereby removing significant amounts of irrelevant information. According to GB 5768.3-2009 National Standard of China [34], the classification of road traffic markings includes categories such as white dashed lines, white solid lines, yellow dashed lines, yellow solid lines, double white dashed lines, double white solid lines, white dash-solid lines, double yellow solid lines, double yellow dashed lines, yellow dash-solid lines, orange dashed lines, orange solid lines, blue dashed lines, and blue solid lines. Among these, the type of lane markings used to separate traffic flows or restrict vehicle crossings include white dashed lines, white solid lines, yellow dashed lines, yellow solid lines, white dash-solid lines, double yellow solid lines, double yellow dashed lines, and yellow dash-solid lines. The white dash-solid lines and yellow dash-solid lines can be further classified as left-dashed right-solid and left-solid right-dashed based on their position in the driving lane. These lane line types are the targets we need to identify in this paper. The dataset categories are annotated, and the label names are shown in Table 4.  
The data images were filtered to exclude road images that do not contain lane lines. Data images with different weather conditions and brightness levels were collected to enhance the model's generalisation capability. After the filtering process, a total of 5480 images containing lane lines were selected. The dataset was then divided into training, validation, and testing sets in an 8:1:1 ratio. The sample distribution of training dataset for each type of lane line is shown in Figure 7.