## 2 Related Work on Pothole Detection
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**References** & **Year** & **Enhancement Techniques** & **Model** & **Image Resolution** & **Inference Speed [FPS]** & **mAP@.5 [\%]** & **mAP@** [0.5:0.95] [\%]** \\ \hline \multirow{2}{*}{\begin{tabular}{c} [PERSON] \\ et al. [31] \\ \end{tabular} } & \multirow{2}{*}{2018} & SSD using & \multirow{2}{*}{300 \(\times\) 300} & \multirow{2}{*}{16} & \multirow{2}{*}{–} & \multirow{2}{*}{–} \\  & & Inception V2 & & & & \\ \hline \multirow{6}{*}{\begin{tabular}{c} [PERSON] [PERSON] \\ et al. [9] \\ \end{tabular} } & \multirow{6}{*}{2020} & SSD using & \multirow{2}{*}{300 \(\times\) 300} & \multirow{2}{*}{33} & \multirow{2}{*}{–} & \multirow{2}{*}{–} \\  & & Inception V2 & & & & \\ \cline{1-1} \cline{5-7}  & & SSD & & & & \\ \cline{1-1} \cline{5-7}  & & MobileNet & & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON] [PERSON] \\ et al. [9] \\ \end{tabular} } & \multirow{3}{*}{2020} & SSD using & \multirow{3}{*}{300 \(\times\) 300} & \multirow{2}{*}{–} & \multirow{2}{*}{45.10} & \multirow{2}{*}{–} \\  & & Inception V2 & & & & \\ \cline{1-1} \cline{5-7}  & & SSD & & & & \\ \cline{1-1} \cline{5-7}  & & SSD & & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON] \\ et al. [29] \\ \end{tabular} } & \multirow{3}{*}{2020} & The system contains & \multirow{3}{*}{LACNN} & \multirow{3}{*}{49} & \multirow{3}{*}{–} & \multirow{3}{*}{–} \\  & localization and part-based & & & & & \\ \cline{1-1} \cline{5-7}  & & classification networks. & & & & \\ \hline Ahmed [11] & 2021 & \multirow{3}{*}{Incorporation of dilated } & YOLOR-W6 & 1774 \(\times\) 2365 & 31 & – & 44.60 \\ \cline{1-1} \cline{5-7}  & & Incorporation of dilated & Faster & & & \\ \cline{1-1} \cline{5-7}  & & convolution (MVGG16). & R-CNN: MVGG16 & & & & \\ \hline \multirow{6}{*}{\begin{tabular}{c} [PERSON] et al. [32] \\ \end{tabular} } & \multirow{3}{*}{2021} & DA: scaling, colour adjustments, rotation, mosaic & YOLOv5 (Ys) & 1774 \(\times\) 2365 & 111 & – & 58.90 \\ \cline{1-1} \cline{5-7}  & & Faster & & & & \\ \cline{1-1} \cline{5-7}  & & R-CNN: ResNet50 & 1774 \(\times\) 2365 & 10 & – & 64.12 \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON] et al. [32] \\ \end{tabular} } & \multirow{3}{*}{2021} & Two cameras with different & \multirow{3}{*}{YOLOv3} & \multirow{3}{*}{416 \(\times\) 416} & \multirow{3}{*}{35} & \multirow{3}{*}{71.00} & \multirow{3}{*}{–} \\  & & fields of view. & & & & \\ \cline{1-1} \cline{5-7}  & & A tracking-via-detection & & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON] \\ et al. [30] \\ \end{tabular} } & \multirow{3}{*}{2021} & YOLOv5s & \multirow{3}{*}{720 \(\times\) 720} & \multirow{3}{*}{–} & \multirow{3}{*}{74.80} & \multirow{3}{*}{–} \\ \cline{1-1} \cline{5-7}  & & YOLOv4 & \multirow{3}{*}{720 \(\times\) 720} & \multirow{3}{*}{–} & \multirow{3}{*}{77.70} & \multirow{3}{*}{–} \\ \cline{1-1} \cline{5-7}  & & YOLOv4- & Tiny & & & \\ \cline{1-1} \cline{5-7}  & & & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON] et al. [32] \\ \end{tabular} } & \multirow{3}{*}{2021} & YOLOv3 & \multirow{3}{*}{416 \(\times\) 416} & \multirow{3}{*}{35} & \multirow{3}{*}{71.00} & \multirow{3}{*}{–} \\  & & A tracking-via-detection & & & & \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} [PERSON] \\ et al. [30] \\ \end{tabular} } & \multirow{3}{*}{2021} & YOLOv5s & \multirow{3}{*}{720 \(\times\) 720} & \multirow{3}{*}{–} & \multirow{3}{*}{74.80} & \multirow{3}{*}{–} \\ \cline{1-1} \cline{5-7}  & & YOLOv4 & \multirow{3}{*}{720 \(\times\) 720} & \multirow{3}{*}{–} & \multirow{3}{*}{77.70} & \multirow{3}{*}{–} \\ \cline{1-1} \cline{5-7}  & & YOLOv4- & Tiny & & & \\ \hline \multirow{3}{*}{
\begin{tabular}{c} [PERSON] et al. [32] \\ \end{tabular} } & \multirow{3}{*}{2021} & YOLOv4 & \multirow{3}{*}{720 \(\times\) 720} & \multirow{3}{*}{–} & \multirow{3}{*}{78.70} & \multirow{3}{*}{–} \\  & & A tracking-via-detection & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of recent pothole detection systems (DA—data augmentation).