Data acqusition challenges in AI-driven surface inspection: a proven solution proposal on coated sheet metal parts

Abstract

This paper introduces an advanced AI-based automated surface inspection system for enhanced quality control in the manufacturing of white-coated sheet metal parts. The study emphasizes overcoming the challenges in AI-driven inspection, such as the need for large datasets corresponding to numerous physical components, which presents storage and logistical issues. By integrating Convolutional Neural Networks (CNNs) and a novel annotation process, the system can be trained effectively on various surface defects. The paper discusses three big data acquisition challenges and provides a solution approach, including large data volumes equivalent to numerous physical components, a novel division of the training process to reduce the workload for domain experts and the relevance of previously clearly defined defect classes.