Generative Adversarial Network Based Deep Learning Method for Machine Vision Inspection

When deep learning methods are applied to the detection of low contrast LCD surfaces, due to the imbalance between positive and negative samples and the difficulty in detecting micro defects with uneven brightness, we propose a method for automatic sample generation and detection based on deep generation network models. Firstly, generate several defect samples by generating adversarial networks to generate an expanded sample dataset. Secondly, the deep generation network is combined with the encoder to form an unsupervised model, and the defective parts of the image are obtained through image comparison. The experimental results confirm that the proposed method can automatically generate LCD image samples, and experiments on Mask R-CNN and unsupervised deep generation network models also confirm the effectiveness of our proposed method.