Reinforcement learning-based approach for plastic texture surface defects inspection

This paper proposes a novel data-enhanced virtual texture generation network for use in deep learning detection systems. The current methods of data enhancement, such as image flipping, scaling ratios, or Generative Adversarial Networks, have limitations as they cannot determine characteristics beyond the training data. The proposed system uses the texture characteristics of a learning surface to generate surface textures through the Open Graphics Library, which can simulate material textures, light sources, and shadow effects. This enables the generation of required texture parameters for the Reinforcement Learning Network to conduct parameter search. The generated image is authenticated by a discriminator, and the reward score is fed back into the critic network to update the value network. The proposed system can complement the imbalance of defective data types, generate large quantities of random and non-defective data, and automatically classify and label during the generation process, reducing labor consumption and improving labeling accuracy. The study found that the proposed data enhancement method can increase the diversity of data characteristics, and the generated data can increase the recall rate of test and verification data sets. Specifically, the proposed system increased the recall rate of test data sets with different distributions from 78.21% to 82.40% and the recall rate of verification data sets with the same distribution from 81.64 to 91.94%.