## 4 Bibliometric Analysis
### Science Mapping Analysis
#### 4.3.3 Co-Occurrence and Timeline View Analysis
The keywords contained within Table 11 are analyzed and the keywords \"deep learning\", \"crack detection\", \"pavement crack detection\" and \"recognition\" all have links of nine, which is associated with all other keywords in the table. This indicates that these keywords are the core keywords in the field of image-based road crack recognition. It is noteworthy that although the keyword \"recognition\" is associated with the remaining nine keywords, the total link strength is only 32. This indicates that it does not appear to be a highly interconnected keyword with other keywords. In addition, the study's authors employed the CiteSpace 6.3.R2 software to enumerate the keywords that appeared with a frequency exceeding a specified threshold. Figure 10 presents a timeline view of the keywords. This shows the trajectory of research and development in the field of image-based road crack recognition from 2013 to 2023. The time \begin{table}
\begin{tabular}{c c c} \hline \hline
**Cluster Color** & **Observed Keywords** & **No. of Keywords** \\ \hline \multirow{4}{*}{Red} & computer vision, feature extraction, image & \\  & segmentation, images, object detection, pavement & \\  & crack, pavement crack detection, recognition, roads, & \\  & segmentation, semantic segmentation & \\  & asphalt pavement, classification, convolutional & \\ Green & neural network, convolutional neural-networks, & 7 \\  & crack detection, damage detection, image processing & \\ Blue & 3d asphalt surfaces, algorithm, deep learning, & \\  & inspection, model, pavement distress, system & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary of clusters obtained from keyword analysis. distribution can be divided into three stages. In the initial stage, spanning from 2013 to 2015, the most pivotal keywords were \"crack detection\", \"image processing and recognition\", \"algorithm\", and \"system\". At this stage, the researchers conducted a comprehensive investigation into the fundamental methodologies for crack detection on roads, with a particular emphasis on ensuring the high applicability of the developed models and methods. The second stage, spanning from 2015 to 2020, reveals a gradual increase in research on image-based road crack detection, with prominent keywords such as \"deep learning\", \"pavement crack detection\", \"damage detection\", and \"convolutional neural network\". This stage has witnessed the introduction of deep learning algorithms in numerous studies, resulting in notable advancements in the identification and classification of road cracks, thereby markedly enhancing detection reliability. In the third stage, spanning the years 2020 to 2023, the most common keywords are \"feature extraction\" and \"semantic segmentation\". The research conducted during this phase will facilitate the development of more sophisticated and accurate systems for monitoring and evaluating cracks in road surfaces. Table 12 presents a list of keywords related to image-based road crack detection, organized by three distinct periods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Keyword** & **Frequency** & **Links** & **Total Link Strength** \\ \hline deep learning & 72 & 9 & 97 \\ crack detection & 71 & 9 & 82 \\ pavement crack detection & 41 & 9 & 51 \\ damage detection & 33 & 8 & 44 \\ convolutional neural network & 23 & 8 & 42 \\ image processing & 23 & 7 & 35 \\ feature extraction & 21 & 8 & 33 \\ recognition & 21 & 9 & 32 \\ system & 21 & 6 & 25 \\ semantic segmentation & 19 & 7 & 33 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Summary of the top 10 keywords. Figure 10: The timeline view of the keywords.