## 4 Results and Discussion
### Image Processing Result  
Identification of the unique visuals of irregularities like illumination, size, and shape are made using image processing techniques. These accurately identify and help classify those irregularities. The irregularity frame selection algorithm is used as a quick video separation of the real-life visual clips captured of the Indian National Highways at several locations, which are divided into two frame categories (frames with irregularities and frames without irregularities). The Hanuman algorithm is responsible for the evaluation of the database of frames with irregularities, which is an automatic technique for the recognition and analysis of potholes and speedbumps. The proto test was conducted using the design described in the image processing methodology and the results for R1 and R2 are shown in Tables 5 and 6. \begin{table}
\begin{tabular}{c c c c c} \hline
**Scenario No.** & **Obstacle Type** & **Depth/Height (cm)** & **Latitude (\({}^{\circ}\)N)** & **Longitudinal (\({}^{\circ}\)E)** \\ \hline
1 & P & 8.8695 & 27.4166 & 76.2903 \\
2 & B & 2.1318 & 27.4507 & 75.7666 \\
3 & B & 8.3590 & 27.6514 & 77.3884 \\
4 & P & 12.9271 & 28.3281 & 76.5420 \\
5 & P & 8.2082 & 27.7750 & 74.2999 \\
6 & P & 7.8984 & 28.2824 & 75.7508 \\
7 & B & 3.0682 & 28.0668 & 74.5035 \\
8 & P & 1.7144 & 28.0620 & 75.3345 \\
9 & B & 3.6010 & 27.9302 & 75.0760 \\
10 & P & 11.6006 & 28.2817 & 75.0858 \\ \hline \end{tabular}
\end{table} 
Table 6: Information of pothole (P) and bumps (B) collected during real-time testing using the image processing method for the R2 test site. Figure 8: Experimental comparison of R1 (Yamuna expressway, urban site) and R2 (potholes on the main road (Khora, Noida, rural site) \begin{table}
\begin{tabular}{c c c c c} \hline
**Scenario No.** & **Obstacle Type** & **Depth/Height (cm)** & **Latitude (\({}^{\circ}\)N)** & **Longitudinal (\({}^{\circ}\)E)** \\ \hline
1 & B & 3.14 & 27.1937 & 76.5725 \\
2 & B & 3.11 & 27.5405 & 76.4872 \\
3 & P & 2.57 & 28.0691 & 76.4799 \\
4 & B & 5.40 & 28.8080 & 76.6505 \\
5 & B & 4.60 & 27.9979 & 77.1004 \\ \hline \end{tabular}
\end{table} 
Table 5: Information of pothole (P) and bumps (B) collected during real-time testing using the image processing method for the R1 test site. The deviation of the location of the pothole is relevant in different methods. The ultrasonic sensor and image processing detection deviated slightly from the actual position. Ergo, to calculate the accuracy of the system, the mean of the output using different method was calculated. The maximum error was 4.60% and the efficiency of the proposed system was about 95.48%. Table 15 shows a full comparison between the results obtained with the proposed methodology and those yielded by similar approaches. As can be seen, our approach clearly outperformed the others, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario No.**} & \multirow{2}{*}{**Obstacle Type**} & \multicolumn{5}{c}{**Latitude (\({}^{\circ}\)N)**} \\ \cline{2-7}  & & **Proposed** & **Image** & **Manual** & **\% Error** & **\% Error** \\  & & **System** & **Processing** & **Inspection** & **(PS vs. IP)** & **(PS vs. MD)** \\ \hline
1 & B & 28.4215 & 27.1937 & 27.41 & 4.32 & 3.56 \\
2 & B & 28.4216 & 27.5405 & 27.25 & 3.1 & 4.12 \\
3 & P & 28.4215 & 28.0691 & 27.53 & 1.24 & 3.14 \\
4 & B & 28.4214 & 28.8080 & 28.00 & \(-\)1.36 & 1.47 \\
5 & B & 28.4214 & 27.9979 & 28.45 & 1.49 & \(-\)0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of longitude between the proposed system (PS), image processing (IP), and manual inspection (MI) for the R1 test site.