Bibliometric Analysis of Road Distress Detection Based on Citespace

To analyze the research progress in the field of road disease detection, this article searched the Web of Science Core Collection database for papers related to road disease detection from 2000 to 2023. Using CiteSpace software, a visual analysis was conducted on the 247 retrieved documents, focusing on annual publication numbers, publishing countries, core institutions and authors, keywords, burst words, and timeline mapping to explore the current status and trends in this field. The findings indicate that since 2004, the overall research trend in road disease detection has been on an upward trajectory, with a notable increase in the number of research papers published in 2021, reflecting a significant surge in research interest. Notably, China, the United States, and Italy have been the most prolific in research output. The leading research institutions are primarily scientific and technological academies, with Chang'an University taking a dominant position in the field of road disease detection. Through keyword co-occurrence analysis, burst detection, and keyword clustering analysis, it was determined that disease detection technology, deep learning and machine learning, image processing and intelligent analysis, and system integration and development have been the research hotspots in the road disease detection field from 2000 to 2023. It is anticipated that future research will evolve towards the directions of performance evaluation, multimodal data fusion, and real-time monitoring and analysis.