# Vision-Based Pavement Marking Detection and Condition Assessment--A Case Study
## 5 Discussion and Limitations  
With regard to the case study results presented in the previous section, this section discusses the limitations of the proposed framework and potential future agendas. Insights into improving the applicability of the proposed scheme are raised based on frequent exploration with our industrial partners as regards practical applications. In the case study, the removal of perspective effects in original video frames relied on the manual selection of feature points. However, human intervention with the aim of obtaining a satisfactory overhead view of road pavement is not a one-off, since the shooting direction of the camera shifts while the vehicle is moving forward. Figure 13 shows two adjacent frames, where there is a sudden change in camera's shooting angle. In addition, incremental deviations from the initial camera settings regarding the lane also have adverse impacts on the performance of pre-processing. As a result, re-calibration of the camera is required so as to successfully remove perspective effects in the captured images. A pre-calibrated camera and its stability throughout recording will greatly facilitate the automation. To apply the proposed condition assessment framework to other pavement markings, such as turning arrows, speed, stop and rail signs, an efficient method to determine their intact contours is essential. Unlike linear markings that have a uniform rectangle shape, other pavement markings tend to present irregular silhouettes due to image distortion and blurring. [PERSON] et al. [45] introduced a generation model to robustly recognize such road markings with changing appearances in a vehicle-mounted camera's view. Alternatively, deep learning algorithms trained on relevant image datasets can replace image processing techniques and automate the identification, yet their expected contours are still required in order to quantify their worn percentage. For this reason, camera calibration with higher precision levels and stability of the camera throughout the data collection stage could pave the way to generating a baseline for the measurement of worn areas with satisfactory levels of precision. This research study focused on pavement marking deterioration caused by traffic wear, which typically manifests as blurring. However, the conditions of pavement markings  
Figure 12: Example frame of false worn condition. (**a**) Planar image; (**b**) binary feature map of the yellow line marking. Figure 13: Example of camera shift during recording. (**a**) Previous frame; (**b**) next frame. are multi-fold. Another vital property of pavement markings is their reflectorization [1]. Reflectorization is the capability of road markings to be seen under dark driving conditions. Currently, road agencies (e.g., Main Roads Western Australia) employ professional consultants to test the level of reflectivity of pavement markings for their roads. Such events require temporary lane closure, special inspection tools and intensive human effort. Future work should aim to develop an automated approach for the quantification of their reflective properties and potentially investigate the relationship between the appearance of road markings in RGB images and their level of reflectorization, thereby expanding the power of vision systems. When it comes to audio-tactile line markings, their functional integrity is to provide sufficient warning to drivers who start to run off their lane. Both their inventory data and condition should be informed by our industrial partners. As inventory data, road agencies are keen to keep distribution information on pavements in their records. For maintenance decision-making, road owners care about the level wear roads have received. [PERSON] and [PERSON] [10] assessed their conditions by measuring the elevation of ribs in 3D laser-scanning profile data. The lack of depth information in 2D visual data, therefore, calls for a dimension-independent indicator for assessment of audible marking conditions in images.