Automotive Paint Defect Classification: Factory-Specific Data Generation using CG Software for Deep-Learning Models

Abstract

In recent years, the advances in technology for detecting paint defects on exterior surfaces of automobiles have led to the emergence of research on automatic classification of defect types using deep learning. To develop a deep-learning model capable of identifying defect types, a large dataset consisting of sequential images of paint defects captured during inspection is required. However, generating such a dataset for each factory using actual measurements is expensive. Therefore, we propose a method for generating datasets to train deep-learning models in each factory by simulating images using computer graphics.