# Automatic Roadway Features Detection with Oriented Object Detection
## 5 Analysis and Results
### Roadway Safety Features Detection
The majority of test data was captured across rural areas except most of the Ring Road which runs through urban areas in the most part. The system predictions confirm this fact, as shown in Figure 6b. Most of the travelled roads in the test dataset have a median, the high accuracy of road separation feature detection is indicated by the green color (high safety factor) in the vast majority of roads in Figure 6c. Predictions visualized in Figure 6d,e confirm the lack of road markings and traffic signs in most of the covered roads; which indicates a needed intervention to enhance roadway safety. Roadside barriers need to be maintained along the Ring Road and need to be constructed along arterial and collector roads, as shown in Figure 6f. As shown in Figure 6f, the traffic condition on all primary highways, arterial roads, and collector roads are predicted to be fluid, because the vast majority of test data was captured over weekends. The majority of test data was captured across rural areas except most of the Ring Road which runs through urban areas in the most part. The system predictions confirm this fact, as shown in Figure 6b. Most of the travelled roads in the test dataset have a median, the high accuracy of road separation feature detection is indicated by the green color (high safety factor) in the vast majority of roads in Figure 6c. Predictions visualized in Figure 6d,e confirm the lack of road markings and traffic signs in most of the covered roads; which indicates a needed intervention to enhance roadway safety. Roadside barriers need to be maintained along the Ring Road and need to be constructed along arterial and collector roads, as shown in Figure 6f. As shown in Figure 6f, the traffic condition on all primary highways, arterial roads, and collector roads are predicted to be fluid, because the vast majority of test data was captured over weekends. The majority of test data was captured across rural areas except most of the Ring Road which runs through urban areas in the most part. Figure 6g, all main roads in GCR are witnessing multitude of billboards which can be a source of driver distraction. As shown in Figure 6h, the system detects an alarming number of pedestrian waiting areas along highways and expressway (particularly the Ring Road), in fact, most of them are not official and there is a need to study their impact on crash numbers. As shown in Figure 6i, the system identifies a large number of heavy vehicles along a regional primary highway, which negatively attributes to roadway safety. As shown in Figure 6j, all roads inside GCR are detected to be covered with light poles which aligns with the urban peripheries of the study area. The resulted maps demonstrate the effectiveness of the proposed system by identifying the roadway segments that have the potential of high safety gains (or issues thereof) to better inform decision makers focusing their efforts on these much-needed features that requires specific interventions. \begin{table}
\begin{tabular}{c c} \hline \hline
**Roadway Safety Feature** & **Prediction Accuracy** \\ \hline Traffic & 81.70\% \\ Area Type & 85.87\% \\ Road Separation & 89.91\% \\ Marking Presence & 90.85\% \\ Traffic Signs Presence & 86.14\% \\ Roadside Barrier Presence & 82.64\% \\ Billboard Presence & 82.23\% \\ Pedestrian Waiting Area Presence & 90.31\% \\ Heavy Vehicle Presence & 63.12\% \\ Light Pole Presence & 91.12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Roadway safety features estimation accuracy. Figure 6: Automatically detected roadway features maps for: (**a**) traffic condition, (**b**) area types, (**c**) road separation, and presence of (**d**) marking, (**e**) traffic signs, (**f**) roadside barrier, (**g**) billboard, (**h**) pedestrian waiting area, (i) heavy vehicles, (**j**) and light poles.