# Automatic Roadway Features Detection with Oriented Object Detection
## 5 Analysis and Results
### Roadway Safety Features Detection  
Table 6 reports the roadway safety features extraction accuracy by comparing the system final predictions on the test data with the recorded ground-truth. Overall, the average accuracy for detection is 84.39%, and it ranges between 91.12% and 63.12%. The most accurate feature detected is the 'light pole presence' and the worst is the 'heavy vehicle presence'. This strongly correlates with the feature corresponding objects' appearance in the dataset; the 'light pole' object appeared 9237 times in our dataset, while the 'heavy vehicle' object appeared only 1347 times. QGIS has been used to visualize resulted KSI's of each roadway safety feature as shown in Figure 6. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Appetences**} & \multirow{2}{*}{\begin{tabular}{c} **Oriented** \\ **Class** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Model 1** \\ **(No Orientation)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Model 2** \\ **(\(\theta\))** \\ **(\(\theta\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Model 3** \\ **(\(\sin(0)\)) and \\ **cos(\(0\))**)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Model 4** \\ **(\(\sin(20)\) and \\ **cos(\(0\))**)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Model 5** \\ **(\(\sin(20)\) and \\ **cos(\(20\)) without** \\ **Augmentation)** \\ \end{tabular} } \\ \hline \hline \multicolumn{8}{c}{Non-Oriented Classes} \\ \hline Traffic Sign & 1413 & No & 35.82 & 44.54 & 41.88 & 20.40 & 39.54 \\ Light Pole & 9327 & No & 59.40 & 56.49 & 60.61 & 56.10 & 62.13 \\ Fence & 657 & No1 & 30.35 & 21.65 & 29.21 & 22.39 & 25.82 \\ Building & 2910 & No & 29.56 & 30.53 & 31.29 & 25.39 & 27.32 \\ Billboard & 5023 & No & 53.10 & 58.08 & 52.94 & 51.05 & 53.51 \\ Pedestrian & 1632 & No & 36.49 & 49.35 & 41.77 & 34.53 & 39.92 \\ Car & 5496 & No & 72.84 & 78.10 & 79.31 & 69.97 & 77.60 \\ Bus\({}^{2}\) & 136 & No & 16.28 & 4.74 & 0 & 0.42 & 8.77 \\ Motorcycle & 234 & No & 51.65 & 67.85 & 48.70 & 48.53 & 46.83 \\ Heavy Vehicle & 1347 & No & 46.01 & 55.18 & 50.36 & 27.04 & 41.05 \\ Public Transportation & 2525 & No & 59.06 & 67.66 & 65.55 & 48.78 & 57.16 \\ Tree & 5628 & No & 37.01 & 35.76 & 37.23 & 30.53 & 38.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(Cont.\)Figure 6 shows a topological map visualizing the automatic safety scoring predictions for each roadway safety feature. In Figure 6a, the traffic condition on all primary highways, arterial roads, and collector roads is predicted to be fluid, because the vast majority of test data was captured over weekends. The majority of the 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 is predicted to be fluid, because the vast majority of test data was captured over weekends. The majority of the 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.