# Smart System to Detect Painting Defects in Shipyards: Vision AI and a Deep-Learning Approach
## 1 Introduction  
The manufacturing industry values not only preparedness for manpower shortages but also responsibility in environmental, social, and governance (ESG) management. For this reason, companies are constantly investing in the construction of smart factories, vision systems, and smart manufacturing using artificial intelligence (AI).  
Recently, most equipment related to manufacturing has been fully connected over a wireless network, monitored by sensors, and controlled by the manufacturing system. Computer intelligence reduces costs associated with the improvement of systems and product quality, thereby promoting productivity and sustainability. It provides various core technologies to cyber-physical systems (CPS) through the internet of things (IoT), cloud computing, and smart technologies. These technologies play a role in solving various problems while developing manufacturing technologies in modern society [1, 2, 3].  
However, sufficient levels of stability and accuracy in production lines cannot be guaranteed by introducing such algorithms at many actual production sites including shipyards. The painting process line of the target shipyard is continuously monitored through CCTV at places that are difficult for humans to access. Such CCTV monitoring enables the determination of defects and the operation of the stop switch. It also completely stops the process line during work breaks or maintenance work. In addition, the absence of a quality management system decreases work efficiency and productivity. To overcome this, methods were proposed for process improvement in industrial sites using various methods.  
In the existing research on defect detection methods, image data are acquired through a machine vision camera or 3D scanner in a controlled environment. Then, the system is studied to determine whether there is a defect using image processing using methods such as Bayesian classifiers, support vector machine classifiers [4], machine learning, or deep learning (DL) [5; 6; 7]. Image processing and machine learning techniques show high accuracy in image data that have fixed locations, shapes, or sizes. However, the accuracy decreases when there are environmental changes, such as the lighting, object size, or object shape. Most of the research studies could control the surrounding environment for the camera and lighting installation to achieve the optimal image data acquisition. However, when applied to an actual shipyard site, there are many restrictions on lighting and camera installations because the object is large and unstructured, and the surrounding equipment is huge. Therefore, a new system using DL was developed for use in industries such as shipbuilding that have unusual data that must be acquired under difficult constraints from the surrounding environments. This improved method was implemented by connecting to the control unit of the actual industrial equipment.  
In this paper, we describe our novel system for the detection of painting defects in the upper and lower parts of steel plates in the process line. Our system incorporated deep learning with vision AI and demonstrably improved the production efficiency and quality of the painting process line by controlling the equipment with a programmable logic controller (PLC) interface.  
The contributions of this study to the production site are summarized as follows:  
1. This is an improved study configured to control the production line by applying deep learning at the actual shipyard production site.
2. It is relevant for conducting research using the unusual datasets acquired at other industrial shipyards.
3. Our system provided additional various services, such as history management and statistical analysis, which met the needs of users.