## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Ref**} & \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Name of the**} & \multirow{2}{*}{**Major Contributions**} & \multirow{2}{*}{**Limitations**} \\  & & **Journal/Conference** & & \\ \hline \multirow{4}{*}{[7]} & \multirow{4}{*}{2011} & \multirow{4}{*}{\begin{tabular}{c} International Journal \\ of Geophysics \\ \end{tabular} } & Introduced multi-scale extraction and & \multirow{4}{*}{\begin{tabular}{c} Method’s dependency on the quality of \\ road texture and limitations of the \\ acquisition system highlighted. \\ \end{tabular} } \\  & & & Markovian segmentation for crack & \\  & & detection in pavements. Offered a new & & \\  & & method and evaluation protocol for & & \\  & & crack detection. & \\ \hline \multirow{4}{*}{[13]} & \multirow{4}{*}{2015} & \multirow{4}{*}{2016} & \multirow{4}{*}{\begin{tabular}{c} Advanced Engineering \\ Informatics \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Comprehensive review of computer vision \\ applications for infrastructure condition \\ assessment. Synthesized state of the art in \\ defect detection. \\ \end{tabular} } \\  & & & & \\ \hline \multirow{4}{*}{[16]} & \multirow{4}{*}{2017} & \multirow{4}{*}{2017} & \multirow{4}{*}{\begin{tabular}{c} Archives of \\ Computational \\ Methods in \\ Engineering \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Comprehensive review of image \\ processing and machine learning \\ approaches for pavement \\ distress detection. \\ \end{tabular} } \\  & & & \\ \hline \multirow{4}{*}{[18]} & \multirow{4}{*}{2018} & \multirow{4}{*}{Data} & \multirow{4}{*}{\begin{tabular}{c} Reviewed applications of deep learning in \\ automated pavement distress detection, \\ comparing deep learning frameworks \\ and architectures. \\ \end{tabular} } \\  & & & \\ \hline \multirow{4}{*}{[20]} & \multirow{4}{*}{2020} & \multirow{4}{*}{IEEE Access} & \multirow{4}{*}{\begin{tabular}{c} Reviewed image processing, machine \\ learning, and 3D imaging methods for \\ crack detection. Compared deep learning \\ neural networks. \\ \end{tabular} } \\  & & & \\ \hline \multirow{4}{*}{[19]} & \multirow{4}{*}{2020} & \multirow{4}{*}{\
\begin{tabular}{c} Journal Of Computing \\ In Civil Engineering \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} Reviewed and evaluated ML-based crack \\ detection algorithms, focusing on \\ pixel-level segmentation. \\ \end{tabular} } \\  & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of previous review of crack detection using image processing techniques. Throughout this evolution, a clear shift from manual and semi-automated techniques to sophisticated deep learning and 3D imaging methods has occurred, reflecting broader trends in technology and data science. Together, these papers emphasize the importance of continued innovation in algorithms, data processing techniques, and sensor technologies to effectively address the challenges of road crack detection. Future research directions focus on enhancing deep learning models, exploiting the potential of 3D imaging, and developing comprehensive systems that integrate various data types and detection methods to assess road conditions more accurately and effectively.