## 5 Conclusions  
In this research, an automatic-image-recognition-based survey system was established on road bridges and was successfully verified through field tests. In order to ensure 1.0 mm resolution performance per pixel in a high-speed (100 km/h) environment, M/V technology using a line-scan camera was used, and accurate images could be obtained almost in real time as a result of the test. Line-scan camera technology advances rapidly, so using a better camera could further improve system performance.  
Technological advances will promote the implementation of the M/V system and lower maintenance costs. Therefore, one contribution of this research is providing a solution that can apply the M/V system to road maintenance using line-scan cameras.  
A survey system equipped with line-scan cameras, GPS, and DMI was designed to perform automatic investigation when accessing the bridge while driving at high speeds. The use of NEXUS for performing automatic surveys of the bridge expansion joint device gap while driving at high speed reduces the survey time by more than 95%, from an average of approximately 1 h/bridge (existing manual inspection method by an inspector) to approximately 3 min/bridge. In addition, if the accumulated gap data is monitored and preemptive maintenance is performed before gap narrowing (lack of joint gap) occurs, it can eliminate the risk factors in the future temperature expansion behavior of the bridge and contribute to traffic safety and cost reductions. This is the second contribution.  
Measuring the bridge expansion joint gap through survey images has limitations in traditional algorithmic methods. Images with various objects on the road surface and shapes of various expansion joint devices are similar to big data. Therefore, by creating an algorithm by artificial intelligence technology (machine learning), more accurate survey values can be stably obtained. The machine learning model for searching for expansion joint devices in survey images used Resnet as a feature extractor, and the representative segmentation model for searching for the gap area used U-Net. These were used to solve the classification problem. Testing with a random selection of 10,526 previously acquired big data images indicated that the expansion gap identification accuracy was improved by 27.5%, from 67.5% to 95.0%. This is another contribution.  
However, the main contribution of this study is to demonstrate the possibility of developing smart maintenance techniques for road structures, in other words, the successful development of a smart maintenance system that combines machine vision and machine learning technology that serves the following purposes: first, to ensure the traffic safety of vehicles on roads that are already in us; and second, to obtain the condition of road structures at the level the investigator wants with figures and accurate images. This requires satisfaction with vehicle traffic, customer safety, investigator convenience, and traffic safety, so we believe that developing road maintenance technology should consider much more complex problems than developing smart construction technology.  
If this study intensifies in the future, we will be able to create a wider variety of smart road maintenance systems based on this concept and our imagination.  
**Author Contributions:** Supervision, investigation, data creation, writing--original draft preparation, and writing--review and editing, I.B.K.; conceptualization, J.S.C. and G.S.Z.; software, formal analysis, validation, and data curation, B.S.C., S.M.L. and H.U.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.