## 4 Experimental Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Test Set** & **Precision** & **Recall** \\ \hline Motorcycle & 85 & 57.10 & 78.43 \\ Car & 9644 & 71.69 & 77.22 \\ Bus & 521 & 54.84 & 59.07 \\ Truck & 583 & 57.57 & 74.43 \\ \hline Total & 10,833 & 70.28 & 77.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Transfer learning result using Faster RCNN. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Test Set** & **Precision** & **Recall** \\ \hline Car & 2198 & 96.9 & 93.2 \\ Bus & 450 & 87.6 & 89.7 \\ Truck & 344 & 98.5 & 92.8 \\ \hline Total & 2992 & 94.3 & 91.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Detection results on highway. Table 10 shows the results when comparing the method using zero padding and the ROI method discussed in Section 3.3. The method using the ROI reduced precision by 0.4 percentage points but increased recall by 4.5 percentage points. It was confirmed that objects that could not previously be detected at a distance were successfully detected, leading to an increase in recall. However, precision decreased due to an increased rate of misdetection of small objects. The proposed method offers the advantage of detecting distant objects and increasing recall, which is why it was used for detecting distant objects. Figure 15 illustrates the obtained results, demonstrating the extraction of objects in various scenarios. Figure 15a illustrates a case with numerous vehicles on a highway, whereas Figure 15b depicts heavy traffic on an urban road. Figure 15c shows a scenario where a motorcycle is detected on red-colored lane, and Figure 15d presents a case where both a truck and an oncoming vehicle are detected on an urban road. Figure 15e presents object detection results at a crossroad. Figure 15f shows detection results under a bridge on an urban road, where minimal shadow and vehicle visibility enable successful detection. Figure 15g,h present the results for misdetection and missed detection. In Figure 15g, a nearby truck under a bridge is detected, but a distant car is not due to poor visibility caused by backlighting. Figure 15h shows a misdetection, where a toll gate is mistakenly identified as an object because its complex structure resembles typical objects of interest. Table 11 presents the lane detection results, which indicate a lane detection rate of 91.52%. Lane detection fails in scenarios with lanes affected by backlighting or blurriness, where edges are not clearly distinguishable, and misdetection occurs at crosswalks and crossroads. Figure 16 illustrates the lane detection cases for various scenarios. Figure 16a depicts multi-yellow lane markings, Figure 16b presents a red lane, Figure 16c shows the presence of shadows, and Figure 16d highlights the center letters. Figure 16e,f illustrate cases of undetected and misdetected lane markings. In Figure 16e, a crosswalk is mistakenly detected as a lane marking, whereas Figure 16f shows the result where lane detection is not performed due to blurred lane markings and light reflection caused by backlighting. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Test Set** & **Precision** & **Recall** \\ \hline Motorcycle & 85 & 85.7 & 84.7 \\ Car & 9644 & 91.1 & 86.3 \\ Bus & 521 & 90.7 & 88.6 \\ Truck & 583 & 90.4 & 88.8 \\ \hline Total & 10,833 & 89.7 & 86.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Detection results of method using ROI. +++ ==ERROR: No output for this page==
a vanishing point, where the blue line represents the extracted distance and the red line indicates the one-dimensional fit. Table 12 presents the mean, variance, and standard deviation of the errors in the above graph. These results indicate that using the vanishing point enables stable distance extraction. Figure 16: Lane detection results: (**a**) multi-yellow lane markings, (**b**) red-colored lane, (**c**) shadows, (**d**) center letters, (**e**) crosswalk, and (**f**) blurry lane markings.