# Smart System to Detect Painting Defects in Shipyards: Vision AI and a Deep-Learning Approach
## 5 Conclusions  
If steel plates are moved to post-processing without detecting and resolving painting defects at an earlier stage, the quality and reliability of the final product are compromised, leading to a waste of time and capital resources. Hence, a system is necessary to detect painting defects at an earlier stage. We developed a DL-based defect-detection system and conducted experiments to evaluate the performance. The following conclusions were obtained as a result. 1. Before the implementation of this defect-detection system, monitoring workers frequently had to either stop the detection equipment if they were performing other maintenance tasks or needed to leave for a while. After incorporating this system, the defects were automatically detected, recorded, and controlled by reducing manual dependency, thereby reducing downtime by 2 h (approximately 11%), including 40 min. of rest and 80 min. of maintenance tasks. In addition, through history management, transparency in quality management was secured, thereby improving the reliability of the steel plates, which in turn boosted the reputation of ship owners and strengthened their competitiveness in winning orders. 2. When a painting defect occurs, sending the defective steel plate to the next process without rework or partial painting will lead to quality mishaps. Prior to implementing this new system, approximately 3% of such quality incidents occurred per quarter. This number was reduced to less than 1% after applying this system. In addition, by managing data, such as the images and frequencies of defects, the causes of these defects can be easily identified, which in turn helps to reduce defect incidence by helping with quick action and maintenance work. 3. During working hours, this new system greatly reduced the physical and psychological fatigue of workers who were constantly immersed in quality management work  
Figure 16: (**a**) Server hardware (**b**) Monitoring at the site. through monitoring. Additionally, end-users of the product reported higher levels of satisfaction. * The previous work method was managed through CCTV for each line. In contrast, through the establishment of our integrated platform and service, the entire line could be monitored from anywhere, laying the foundation for responding to shortages of manpower. * Although various models of deep learning operate within generally known characteristics, in the case of industrial sites, the results of these models also vary with the object shapes and characteristics of the data. When the dataset is not standardized, existing models are selected based on known characteristics. However, it is advantageous to select as many models as possible and extensively experiment with them for fine-tuning. * The most challenging aspect of deploying the developed AI system is to form a positive change in people's perceptions. Although AI has recently become a prominent issue, AI has been regarded as a distant future story in many actual industrial sites. It is also true that there are many negative perceptions. Managers doubted the reliability of AI, and workers were afraid of losing their jobs. However, with the final developed AI system, it was recognized that AI can help workers rather than replace workers while improving productivity and quality with sufficient reliability. The limitation of this study is that there are various types of defects, but sufficient data are not always secured for each defect, so they are classified as one class. Then the AI work is judged as defective based on repainting and specimen tests. In the future, the reliability and accuracy of the system can be improved by developing a model to accurately verify the location and shape of the defect using semantic or instance-segmentation techniques. In addition, we intend to study the effect of data deficiency on accuracy by applying data augmentation techniques using generative adversarial networks and will continue to optimize resources consumed through continuous model weight reduction using TensorRT algorithms.