# Synthetic Data and Computer-Vision-Based Automated Quality Inspection System for Reused Scaffolding
## 1 Introduction  
In the construction industry, accidents are not uncommon and lead to many dangerous situations, including fatalities. One of the causes of accidents is the collapse of scaffolding, which occurs due to poor quality. According to the Bureau of Labor Statistics' Census of Fatal Occupational Injuries [1], more than 500 people died from scaffolding injuries in the United States between 2011 and 2018, an average of more than 65 people per year. In Korea, 241 people died from the same cause in 2019 [2]. Quality control of temporary equipment in Korea is under the general management and supervision of the Ministry of Employment and Labor, which oversees workplace safety in accordance with the Industrial Safety and Health Act. Nevertheless, a significant amount of scaffolding is reused without proper quality control, which affects the quality of construction work and its safety. Moreover, it is difficult to maintain and define performance standards for the distribution and use of temporary equipment [2]. Additionally, it is difficult for regulators and consumers to distinguish between illegal and defective products. It is also difficult for regulators and consumers to distinguish between illegal and defective products. Manufacturers and distributors of temporary equipment are often small and medium-sized companies that do not typically invest in safety and in improving the quality of temporary equipment. Construction companies might argue that building and operating AI in scaffolding could sharply increase costs. Indicators from other companies [3] that have implemented AI in their process confirm the effectiveness and benefits of the technology.  
Systems for monitoring the safety of workers [4], the safe movement of moving objects on the construction site [5], video analysis to eliminate unfavorable situations in production [6], video monitoring of product quality, monitoring the operation of production equipment, and work optimization of production personnel not only ensure the safety of workers, but also significantly improve the quality of work and the final product.  
Currently, the evaluation of the quality of the scaffold is carried out only visually by people, as there are no technologies that ensure automation. However, quality control based on computer vision is successfully used in various fields. According to the indicators from other companies [3] that have implemented AI in their process, the effectiveness and profitability of the technology were confirmed.  
There are several challenges to implementing automation in scaffolding inspection:  
1. The lack of a regulatory framework that defines the quantitative indicators of the extent of the deformation and its impact on the quality of the scaffold.
2. The lack of technologies that allow the automatic detection of different types of deformations necessary for quality assessment, which can be combined into an overall assessment.  
This study aimed to quantitatively and objectively improve the existing evaluation of scaffold quality using machine learning algorithms, which can avoid most human errors. In addition, our study presents and explains the first automatic defect detection solution for reused scaffold quality assessment based on 2D and 3D data. There are several important areas where the technologies in this study make an original contribution to the inspection of used scaffolds:  
1. Improvement of the existing inspection process by reducing the time for quality inspection and independence from human errors.
2. Describing an augmentation process of the real dataset by synthetic data prevents over-fitting of the model during the training phase and reduces the time needed for the preparation of the dataset.
3. Describing the advantages of using dice loss for mask branch or mask R-CNN models.
4. Defining the scaffolding quality classes as a function of the deformation values obtained from the expert survey.