### Automated Pavement Inspection Methods in Different Countries  
* [PERSON] [12] utilized a self-developed Automated Pavement Damage Image Detection System (APDIDS) for automated pavement surveys. The system includes two main designs: the first is a camera bracket fixed to the vehicle body and a camera housing mounted on the bracket, and the second is a distance sensor mounted on the wheel that rotates with it. After processing the pavement images obtained, steps such as pavement image synthesis, lighting correction, binarization, and damage enhancement are performed to obtain better images. Then, the pavement damage image extraction and classification methods are used for recognition, followed by relevant calculations and analysis of the recognition results, as shown in Figure 1. The system can recognize transverse cracks, longitudinal cracks, alligator cracking, patches, and potholes with a recognition rate of up to 91%. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Grading** & **Light Rutting** & **Moderate Rutting** & **Severe Rutting** \\ \hline Rutting Depth (mm) & 6–13 & 13–25 & \textgreater{}25 \\ \hline (Source: Compiled from ASTM D6433, 2023 [10]). & & \\ \end{tabular}
\end{table} 
Table 4: Rutting depth grading intervals. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Grading**} & \multirow{2}{*}{**Maintenance Method**} & \multicolumn{3}{c}{**Crack Rate (\%)**} & \multicolumn{3}{c}{**Rutting (mm)**} \\ \cline{3-6}  & & **Average Crack Rate (\%)** & **Maximum Crack Rate (\%)** & **Average Rutting Depth (mm)** & **Maximum Rutting Depth (mm)** \\ \hline A & Emergency & & & & \\ Repair & - & \textgreater{}30 & - & - \\ \hline B1 & Preventive Maintenance & \textgreater{}20 & 25–30 & \textgreater{}20 & \textgreater{}25 \\ \hline B2 & Maintenance & & & & \\ Required & 18–20 & 20–25 & 18–20 & 20–25 \\ \hline B3 & Track Required & 16–18 & 16–20 & 16–18 & 16–20 \\ \hline C & Regular Tracking & 12–16 & 12–16 & 12–16 & 12–16 \\ \hline D & Good Condition & \textless{}12 & \textless{}12 & \textless{}12 & \textless{}12 \\ \hline \hline \end{tabular} (Source: Compiled from the Metropolitan Expressway Company, 2017 [9]).  
\end{table} 
Table 3: Criteria for determining pavement inspection results on the Metropolitan Expressway in Japan. \begin{table}
\begin{tabular}{c c} \hline \hline
**Road Grade** & **IRI (m/km)** \\ \hline Airport Runways, Expressways & 0.25–1.75 \\ New Pavement & 1.25–3.50 \\ Old Pavement & 2.25–5.75 \\ Well-Maintained Unpaved Roads & 3.25–10.00 \\ Damaged Roads & 4.00–11.00 \\ Rough Unpaved Roads & \textgreater{}7.75 \\ \hline \hline \end{tabular} (Source: Compiled from World Bank Technical Paper, 1990 [11]).  
\end{table} 
Table 5: Relationship between road grade and IRI. * The Australian Road Research Board (ARRB) has been committed to the development of pavement inspection equipment for over 50 years. The Hawkeye Automated Detection Vehicle is highly functional, capturing road information used by engineers and road users. Its functions include highway pavement asset management, a pavement image capture system, a digital profile elevation system, a GPS/DGPS system, and a DIS system. Figure 2. shows the Hawkeye 2000 detection vehicle. The equipment includes two high-performance 3D laser elevation sensors mounted on the rear of the survey vehicle, positioned vertically above the pavement. Each laser elevation sensor includes a high-power laser and a 3D camera mounted on a rotating laser axis, where the laser light and camera images are used to measure pavement transverse profiles with a resolution of 0.5 mm [13]. * [PERSON] [14] used images captured by a dashcam to recognize pavement damage, employing SLIC Superpixels as the main recognition principle. Through a two-stage image clustering process, pavement damage in the images is identified. The damage clusters are then classified to identify patches, potholes, longitudinal and transverse cracks, and alligator cracking, integrating PCI numerical calculations.