# Synthetic Data and Computer-Vision-Based Automated Quality Inspection System for Reused Scaffolding
## 2 Research Background
### 2D Image-Based Defect Detection  
One of the valuable methods for detecting defects on a metal surface was proposed by [PERSON] [19]. The researchers used a convolutional neural network based on YOLO architecture, which detected defects in real time with high accuracy on steel trips; however, this method had a strong limitation in calculating the area of defects. Another approach was presented by [PERSON] [20]. According to the study, the main proposal was a semi-supervised semantic segmentation network for concrete crack detection based on two neural network methods: student and teacher models. The two models have the same network structure and use the Efficient UNet architecture to extract multi-scale crack feature information that reduces the loss of image information. The proposed model had high accuracy in a crack-detection task. Solutions for real-time defect detection were proposed by [PERSON] [21]. The author developed PipeUNet for sewer defect segmentation. The model used CCTV images to detect cracks, infiltrations, and joint offsets. It worked with a mean IOU of 76.37 accuracies. However, during the real-time defect detection, accuracy was lower than for the non-real-time model. This can be explained by limitations of control and interaction between quality of scanning and speed. In 2020, [PERSON] [22] proposed a method based on high dynamic range image analysis and processing to solve the problem of visual inspection of industrial parts with a highly reflective surface. The research focused on image color correction and adaptive segmentation.  
Manual quality control is still prevalent in many types of production, as any industrial enterprise has a whole staff of trained employees and honed quality standards. However, automated visual flaw detection based on computer vision can significantly reduce the direct participation of workers in the quality control process on all types of production lines. Table 1 summarizes the above research, with the methods divided into two groups: 3D laser-scanning-based and 2D image-recognition-based detection. Each method has its strengths and weaknesses. Laser scanning methods are more accurate, but generally do not detect rust unless a surface is deformed. On the other hand, methods based on 2D image recognition can visually detect such defects as rust, paint stains, and dirt. However, they are less accurate and more dependent on the imaging conditions, the illumination quality, and the reflection of a metal surface.  
Furthermore, with only 2D images, it is more complicated to detect surface deformations. In consequence, it was decided to use 2D and 3D scanning methods together to exploit their strengths and compensate for their shortcomings. The current study proposed a detection method for rusted areas based on image segmentation and for deformation and cracks based on point cloud analysis. In many manufacturing companies, quality control is still a manual operation. Even though humans are highly capable of visually inspecting a wide range of objects, subjectivity and fatigue can lead to mistakes when performing repetitive tasks. In contrast, CV-based automation systems offer an effective solution for quality and process control, improving the efficiency of inspection operations by increasing productivity and process accuracy, and reducing operating costs. For detecting detective objects, the CV-based automated system uses a camera that captures images, which are then compared algorithmically with predefined images.