Defect Detection for Enhanced Traceability in Naval Construction

The digitalization of shipbuilding processes has become an important trend in modern naval construction, enabling more efficient design, assembly, and maintenance operations. A key aspect of this digital transformation is traceability, which ensures that every component and step in the shipbuilding process can be accurately tracked and managed. Traceability is critical for quality assurance, safety, and operational efficiency, especially when it comes to identifying and addressing defects that may arise during construction. In this context, defect traceability plays a key role, enabling manufacturers to track the origin, type, and evolution of issues throughout the production process, which are fundamental for maintaining structural integrity and preventing failures. In this paper, we focus on the detection of defects in minor and simple pre-assemblies, which are among the smallest components that form the building blocks of ship assemblies. These components are essential to the larger shipbuilding process, yet their defects can propagate and lead to more significant issues in the overall assembly if left unaddressed. For that reason, we propose an intelligent approach to defect detection in minor and simple pre-assembly pieces by implementing unsupervised learning with convolutional autoencoders (CAEs). Specifically, we evaluate the performance of five different CAEs: BaseLineCAE, InceptionCAE, SkipCAE, ResNetCAE, and MVTecCAE, to detect overshooting defects in these components. Our methodology focuses on automated defect identification, providing a scalable and efficient solution to quality control in the shipbuilding process.