## 3 Research Methodology  
The authors used a combination of methods in this work, including a bibliometric analysis and a critical analysis of papers. The main focus of the study is on the algorithms used for road crack detection. Figure 1 illustrates the overall technique used in this research. As shown in Figure 1, the first stage is the collection of the original paper data for this review. The second stage is a bibliometric analysis to identify key areas of research. The third stage involves a careful evaluation of the papers, focusing on the abstract, methodology, and results. In addition, a brief summary of the progress made in the development of algorithms for road crack detection is provided. In order to conduct a comprehensive literature review, the authors followed a predetermined set of criteria to include the most relevant scientific papers. Figure 2 shows the process of document retrieval and data filtering, referred to as stage 1 in Figure 1. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, there are four separate steps in the data collection process: identification, screening, eligibility, and inclusion [23]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Name of the**} & \multirow{2}{*}{**Major Contributions**} & \multirow{2}{*}{**Limitations**} \\  & & **Journal/Conference** & & & \\ \hline \multirow{2}{*}{[22]} & \multirow{2}{*}{2021} & \multirow{2}{*}{Engineering} & Summarized state-of-the-art in sensing & Suggested improvement in sensors and \\  & & & techniques, image processing, and ML & algorithms for better adaptability and \\  & & & methods for pavement monitoring. & performance. \\ \hline \multirow{2}{*}{[21]} & \multirow{2}{*}{2022} & \multirow{2}{*}{\begin{tabular}{c} Construction And \\ Building Materials \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} Focused on image segmentation \\ approaches in crack detection, reviewing \\ thresholding-based, edge-based, and \\ data-driven methods. \\ \end{tabular} } &
\begin{tabular}{c} Highlighted the need for advancements in \\ algorithms to handle diverse pavement \\ conditions and complex textures. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 1: Overview of the research methodology. Phase 1: In March 2024, the authors searched for papers on Scopus. To discuss the development of crack detection and the latest technologies, the author restricted the search string to a period from 2013 to 2023, using the search keywords \"pavement crack detection\". Only articles were selected as the document type to ensure that it was an original study. At the end of the first phase, the authors identified a total of 607 papers. Phase 2: The authors filtered eligible papers from the 607 extracted papers by title and abstract. In order to avoid the inclusion of irrelevant papers, the authors established rules to exclude and discard these papers if (a) the focus of a particular paper was a non-image-based crack detection algorithm, (b) the paper dealt with the detection of non-road cracks (such as walls, steel, etc. ), (c) the paper investigated the method of detecting non-surface road cracks. The application of these rules resulted in the exclusion of 218 papers at this stage. Phase 3: At this stage, the remaining 389 papers were evaluated using the full text of the survey. Papers that were not related to the research topic of this study or that did not make an innovative and effective contribution to the field of image-based crack detection research were eliminated. In sum, 84 papers were excluded. This reduced the number of papers extracted to 305.