基于NVAE和OB-Mix的小样本数据增强方法OACSTPCD

Due to the high dependence of deep learning models on massive labeled data,many cutting-edge target detec-tion theories are difficult to apply to the field of industrial detection.To this end,a small-sample data augmentation method based on NVAE for image generation and OB-Mix for data regularization is proposed.The specific method is to build a data distribution model of the detection target images through NVAE,and then generate new target images that belong to the same distribution as the real target images by sampling latent variables.After the generated target images are obtained,an OB-Mix data augmentation strategy is proposed,which mixes the generated target images with the background images at random positions to construct new images data,thereby improving the localization ability and generalization ability of the network.In the case of using only 474 labeled images and 400 background images without detection targets,the detec-tion Precision of YOLOv5 reaches 95.86%,which is 17.60 percentage points higher than the training without this method.