Smart System to Detect Painting Defects in Shipyards: Vision AI and a Deep-Learning Approach

Abstract

The shipbuilding industry has recently had to address several problems, such as improving productivity and overcoming the limitations of existing worker-dependent defect-inspection systems for painting on large steel plates while meeting the demands for information and smart-factory systems for quality management. The target shipyard previously used human visual inspection and there was no system to manage defect frequency, type, or history. This is challenging because these defects can have different sizes, shapes, and locations. In addition, the shipyard environment is variable and limits the options for camera placements. To solve these problems, we developed a new Vision AI deep-learning system for detecting painting defects in an actual shipyard production line and conducted experiments to optimize and evaluate the performance. We then configured and installed the Vision AI system to control the actual shipyard production line through a programmable logic controller interface. The installed system analyzes images in real-time and is expected to improve productivity by 11% and reduce quality incidents by 2%. This is the first practical application of AI operating in conjunction with the control unit of the actual shipyard production line. The lessons learned here can be applied to other industrial systems.

Keywords

• smart factory
• deep learning
• steel plate painting
• painting defect detection
• production line programmable logic controller