## 4 Bibliometric Analysis
### Science Mapping Analysis
#### 4.3.3 Co-Occurrence and Timeline View Analysis  
Keywords in a research paper serve an essential function in elucidating a paper's research focus. In this section, the authors conducted a co-occurrence analysis of keywords to analyze the research trends and to identify and research hotspots in the field of image-based road crack detection algorithms. A total of 815 keywords were obtained from 305 publications using VOSviewer 1.6.20. The authors established a threshold of 10 for the minimum number of occurrences of a keyword. A total of 25 of the 815 keywords met the threshold. Figure 9 presents a network visualization of the paper's keyword co-occurrences. And Figure 9 is summarized in Table 10.  
The circular nodes depicted in Figure 9 represent distinct keywords. The size of the nodes indicates the frequency of occurrence of the keywords. Therefore, the larger the node, the more frequently and importantly the keyword is used. The size of the circle indicates the frequency of occurrence of the keyword in the paper. A smaller circle indicates a lower frequency of occurrence. The line between two nodes represents the number of times the two keywords appear together. The thicker the line, the more often the two keywords appear together in a paper. Conversely, the thinner the line between two nodes, the less often the two keywords appear together. Figure 9 illustrates  
Figure 9: Network visualization of co-occurrences of the keywords. the most frequently occurring keyword, appearing 72 times. The second most frequently occurring keyword is \"crack detection\", which appears 71 times. The third-ranked keyword is \"pavement crack detection\", which has appeared 41 times. It is noteworthy that two keywords with a high degree of similarity, namely \"convolutional neural network\" and \"convolutional neural networks\", were identified during the keyword analysis process. When combined into a single keyword, it ranks fourth with 35 occurrences. The keyword \"damage detection\" was identified 33 times and ranked fifth. The node \"deep learning\" is most closely associated with \"crack detection\", \"pavement crack detection\", and \"damage detection\", as evidenced by the thickest connecting lines between these three nodes. Deep learning is a widely used technique in road crack detection. Figure 9 illustrates that the red cluster, comprising 11 keywords, is the largest of the three clusters. The keyword \"pavement crack detection\" is the largest for the nodes in the red cluster. This keyword is observed 41 times. The other keywords with a greater number of occurrences in this cluster are \"feature extraction\" and \"image segmentation\", with 21 and 17 occurrences, respectively. The keywords in the red cluster underscore the significance of feature extraction and image segmentation techniques in the recognition and analysis of road conditions. The primary keyword in the green cluster is \"crack detection\" which occurs a total of 71 times, along with \"damage detection\", \"convolutional neural network\" and \"image segmentation\" which occur 21, 17, and 17 times, respectively. The combination of \"convolutional neural network\" and \"image processing\" serves to highlight the significance of employing convolutional neural network techniques in the context of damage detection and image processing. The keyword \"deep learning\" is the most prominent in the blue cluster, occurring 72 times and representing the most notable keyword in Figure 9. The keywords in the blue cluster collectively emphasize the pivotal role of deep learning in enhancing algorithmic efficiency, model accuracy, and overall system performance, particularly in an environment where complex data analysis is a significant consideration. Following the completion of the cluster analysis, the authors proceeded to extract the 10 most frequently occurring keywords. Table 11 provides a summary of the keywords, frequency, links, and total link strength. The table was sorted by keyword frequency.