# Vision-Based Pavement Marking Detection and Condition Assessment--A Case Study
## 4 Case Study Results
### Line Marking Detection and Classification  
The video data in this case study included five types of longitudinal line markings, namely, edge line, continuity line, transition line, lane line and dividing line. They were assigned to four classes, as indicated in Table 2. An additional category for classification was introduced to represent non-marking objects, such as road-side railings, curbs and other vehicles that appear in the region-of-interest. A threshold-based method was utilized based on distinctive shape features, where dividing lines (code \"01\") and continuity lines (code \"04\") are both broken markings with different length of strips and gaps, while marking types in code \"02\" all appear to be single continuous lines. The ground truth of line marking distributions along the test road was collected from the authority. A comparison between the classification results and the ground truth was conducted to evaluate the performance of the proposed methods. Accuracy, as the ratio of successfully classified samples to all samples, was used for evaluation.  
Table 2 shows the performance of the threshold-based method for line marking classification. As for the detection of audible markings on top of line markings, the accuracy is 0.82 for the near-field instances. This research was then compared to an existing piece of research [10], which used laser-scanning 3D profile data to detect and measure road markings and achieved a 90.8% level of accuracy for detection. It is worth noting that 3D profile data possess the ability to overcome the impacts of shadows, varying illuminations and occlusions, and provide additional information, such as elevation. Using 2D optical images, this research achieves an average accuracy of 89% and presents a satisfactory performance.  
A range of environmental factors account for the detection failures. The road segment in Figure 8 has an extensively polished pavement surface and patches, generating a misleading feature map (shown in the right image). As a result, both the left edge line and the dividing line were mistakenly identified as \"Other\". Another notable disturbance hindering accurate classification is vehicles on adjacent lanes, which causes severe occlusion. Such situation typically occurs when overtaking is occurring, and thus, it lasts no more than five frames. Temporary loss of line marking detection in some image frames can be supplemented by referring to their context in the time sequence. Curbs and railings alongside the road, as shown in Figure 9b,c, can also lead to detection errors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Code** & **01** & **02** & **03** & **04** & **Overall** \\ \hline \multirow{3}{*}{Type(s)} & \multirow{3}{*}{Dividing line} & Edge line; & & \\  & & Transition line; & & \\  & & Lane line & & \\ \hline Accuracy & 0.92 & 0.87 & 0.85 & 0.92 & 0.89 \\ \hline \hline \end{tabular}  
* Accuracy is the ratio of successfully classified samples to all samples.  
\end{table}
Table 2: Line marking classification results.  
Figure 8: Example of polished pavement.