# Vision System in Product Quality Control Systems
## 2 Vision System in the Quality Control Systems
All systems, regardless of the imaging method used or the image analysis algorithm employed, work within the TQM framework and ensure the quality of the raw material received for production. A similar development of vision technology has been observed in the woodworking industry. The first scientific papers and implementations discussed using 2D monochrome and color images for wood surface analysis and surface defect detection using classical image analysis algorithms [38]. Subsequent studies extended the measurement methods to 3D solutions [39]. These were used, among others, to study wood growth and assess the wood surface defects [40; 41]. The next phase of developing vision systems in the industry saw implementations using machine learning methods [42]. However, the most dynamic development of vision systems was observed in the automotive sector. It can be assumed that every newly designed production line and the technological station is equipped with a set of vision systems controlling the manufactured product and process parameters. Vision systems (2D and 3D) are used together with industrial robots to locate semi-finished products in bins and conveyor belts. They are therefore involved in the production process. In addition, they are also used in product inspection operations, where they carry out measurement tasks [43; 44]. Within the scope of 3D imaging, control and measurement systems based on laser triangulation, stereovision, and the TOF method are applied in production processes [45; 46; 47; 48] (Figure 1). The food and waste sorting industry has also seen a very rapid development of hyperspectral imaging in implementing inspection tasks in the production process. This imaging method has made it possible to observe the product in multiple ranges of electromagnetic radiation and to identify correlations between selected features of the object and the image in the selected imaging range. Waste sorting is an exciting industry where the combination of hyperspectral imaging with artificial intelligence methods has been observed [49; 50; 51]. Additionally, combined with AI solutions, hyperspectral imaging will be a key technology in detecting and assessing defects in materials and products. The trend of introducing vision measurement and control systems to production lines observed in recent years will also continue in the years to come. According to the Frost and Sullivan report [52], the automotive industry will still have the largest share. However, the second and third places will be taken by the pharmaceutical and medical industries. This information is referred to in the year 2020 as the base (Figure 2). One of the constraints on the development of vision-based quality control systems is the variability and intermittent difficulties in the availability of high-resolution matrices with the parameters needed to build measurement systems. For example, the impact of COVID-19 on the production of components for vision systems can be seen in 2021-2022. Figure 1: Methods of acquisition of three-dimensional images. Currently, the role of vision systems as data sources in the automation of quality control is growing. This is particularly noticeable in factories using database systems and process data analysis algorithms working within Smart Industry. The advantage of this solution is that the image analysis and description of the product quality in the form of parameters are carried out directly by the vision system on a production line. A report by C. Barnes and Co Barnes and Co Barnes and Co (2018) on the development of the vision systems industry in 140 countries indicated an increase in the use of vision systems due to the need for the automation of manufacturing processes. This is connected to the necessity for the realization of automatic product control, process control, and robotization. The World Robotics 2021 report and the experience gained during implementations indicate that the number of robots is growing in practically every manufacturing industry.