Synthetic Data Generation for Surface Defect Detection

Abstract

Ensuring continued quality is challenging, especially when the customer satisfaction is basically the provided service. It seems to become easier with new technologies like Artificial Intelligence. But to design an intelligent assistant, field data are necessary but not always available. Synthetic data are largely used to replace real data. Made with a Generative Adversarial Networks or a rendering engine, they aim to be as efficient as real ones to train a Neural Network. When synthetic data generation meet the challenge of object detection, its capacity to deal with defect detection challenge is unknown. Here we demonstrate how to generate these synthetic data to detect defects. Through iterations we apply different methods from literature to generate synthetic data for object detection. From how to extract a defect from the few data we have, to how to organize the scene before data synthesis. Our study suggests that defect detection may be performed by training an object detector neural network with synthetic data and gives a protocol to do so even if at this point, no field experiments have been conducted to verify our detector performances under real conditions. This experiment is the starting point to develop a mobile and automatic defect detector that might be adapted to ensure new product quality.