DCGAN-Based Generation of Ultraviolet Image Intensifier Field-of-View Defect Images

Traditional data enhancement methods are easy to over-fit. To solve the problem of sample imbalance in the field of view defect image dataset of the ultraviolet image intensifier and improve the recognition accuracy of stripe defects based on deep learning, a field of view defect image generation method of the ultraviolet image intensifier based on a deep convolution generative adversarial network (DCGAN) is proposed. Through the improvement of the loss function of the DCGAN and the optimization of the convolution attention mechanism, the generation model of the field-of-view defect image of the UV image intensifier is established, and the generation of the field-of-view defect image of the UV image intensifier is successfully realized. The image quality evaluation index and defect detection models are then used to verify the effectiveness of the generated image. The experimental results show that the generated UV image intensifier field-of-view defect image can meet the application requirements, and the detection accuracy can be improved by fusing the generated image into the real image and then entering the defect detection model. The research results provide technical support for field-of-view defect detection based on the deep learning of the third-generation low-light-level image intensifier and ultraviolet image intensifier.