## 5 Conclusions  
In this study, a total of 2000 samples of hazardous and non-hazardous gaps were input from the system for 50,000 iterations. The batch size was adjusted to 128 units. Effective accuracy of 89.06% can be obtained through model training. When people go to deep mountains or slopes for inspection, they must stand above or below the retaining wall and use a ruler to inspect visually. Often restricted by terrain, it is not easy to judge. Comparing the experimental and predicted results of crack width measurement, the difference is less than 1.5%. However, the overall measurement is performed with certainty, thus avoiding human error or oversight and uncertainty. This study implements a Fast R-CNN-based deep learning module and the \"gap hazard evaluation method (GHEM)\". The expansion of the database effectively improves the efficiency of gap recognition, and the post-processing and feature extraction of the segmented images have achieved good results. The weight function is obtained by linear regression of the data and is used to adjust the unreasonable weight distribution in the equation to make the system more stable. The detection process is about 20-25 frames per second, and the processing time is about 0.04 s. In capturing packets, there will still be errors in judgment and improper circle selection. The false positive rate was between 2.1% and 2.6%. Although deep learning architectures effectively extract the components of seams in images, their efficiency is not maximized; when combined with the innovative \"gap hazard evaluation method\", unreasonable false positives can be more effectively avoided. If used for real-time detection, time-consuming or low frame rate contour image recognition may be encountered. The complete database and weight distribution can be adjusted to give the module better recognition efficiency, or other neural network architectures can be tried to improve the deficiencies of existing modules.  
Regarding the system, in the methods of the GHEM Fast R-CNN, this work is performed on the entire system, and pre-processing is performed first. On the basis of Fast R-CNN, CNN, and ReLU resampling, feature points, and line regression are added to enhance processing and the GHEM. This system finally uses Softmax as an activation function and NMS, which can detect cracks more effectively.  
Conceptualization, C.-S.L, D.-H.M, and Y.-C.W.; methodology, D.-H.M., Y.-C.W., and C.-S.L; software, D.-H.M.; validation, Y.-C.W., C.-S.L, and D.-H.M.; formal analysis, D.-H.M., Y.-C.W. and C.-S.L; investigation, Y.-C.W., D.-H.M.; resources, C.-S.L; data curation, D.-H.M., Y.-C.W.; writing--original draft preparation, Y.-C.W., D.-H.M., and C.-S.L.; writing--review and editing, C.-S.L., D.-H.M., and Y.-C.W.; visualization, D.-H.M., Y.-C.W.; supervision, C.-S.L.; project administration, C.-S.L. and D.-H.M.; funding acquisition, C.-S.L., and D.-H.M. All authors have read and agreed to the published version of the manuscripts.  
Figure 21: Schematic diagram of fitting 20 \(R_{t}\) data diagram.  
**Funding:** This research was funded by the Ministry of Science and Technology under Grant No. MOST 111-2221-E-035-067.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** Thanks to [PERSON] for assisting with the development environment setup and some data acquisition.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.