# Synthetic Data and Computer-Vision-Based Automated Quality Inspection System for Reused Scaffolding
## 6 Conclusions  
Safety at the construction site depends on the quality of temporary structures. Therefore, periodic scaffolding quality inspection is essential. However, current methods involve only subjective assessment by workers, which has specific weaknesses.  
The current study aimed to improve the quality evaluation system for temporary structures to evaluate the quality of scaffolding parts necessary to measure such parameters as rusty areas, cracks, and surface deformations. Recently, computer vision approaches have been widely used in various industries, and are relevant solutions in the construction field. Thus, this study proposed an automated quality inspection technology based on 2D and 3D data processing by ML algorithm.  
The solution included two parts. Firstly, Mask R-CNN model-based convolutional neural network was used for recognizing rusty areas on images. The model training was performed on a combined real and synthetic dataset to solve the overfitting problem. The use of synthetic data improved the performance of neural networks and various machine learning algorithms, allowed reduction of the percentage of distortions, and significantly  
Figure 20: Prototype of graphical user interface.  
reduced the amount of necessary \"real\" data, saving time and money for the customer. Since the generator allows the obtainment of 3D data, it can become a source of new data not only for the task of classical computer vision, but also for several tasks of geometric computer vision.  
Secondly, point-to-point differences were calculated, and classification of defects was carried out for the purpose of deformation and crack detection on point cloud. The use of a point cloud for deformation detection obtains more accurate results than 2D images, but the method has a limitation in detection of trusted areas and more expensive computational costs.  
The findings of the current study present a few recommendations and options for improving the current inspections. Despite their various limitations, new methods are extremely important for future studies. The benefits of the new methodology were significant during the experiments, and we expect that they will be realized in the future.  
Conceptualization, A.K., S.L., J.S. and S.C.; data curation, A.K.; formal analysis, A.K.; investigation, A.K.; methodology, A.K.; project administration, K.L. and S.K.; resources, A.K.; software, A.K.; supervision, K.L. and S.K.; validation, A.K.; visualization, A.K.; writing--original draft, A.K.; writing--review & editing, A.K. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Korea Ministry of Land, Infrastructure and Transport (MOLIT) under the \"Innovative Talent Education Program for Smart City\". This research was conducted with the support of the \"National R&D Project for Smart Construction Technology (No. 21 SMIP-A158708-03)\" funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport, and managed by the Korea Expressway Corporation.  
Not applicable.  
The authors declare no conflict of interest.