A transformed-feature-space data augmentation method for defect segmentation

Abstract

Data augmentation is widely used in convolutional neural network (CNN) models to improve the performance of downstream tasks. The images generated by traditional data augmentation methods are usually random and can struggle to overcome the range limitation of the sampled data distribution, which can result in meaningless augmented data and poor diversity. As a result, a novel data augmentation algorithm is proposed to generate images that are outside the scope of the sampled data distribution along the feature direction with diversity weights. First, an image is mapped to the latent feature space based on the encoder. Then, the range loss function can restrict the latent variables within a hypersphere of radius k. The feature directions and diversity weights can be found based on the entropy variation in the latent feature space. During the CNN model’s training process, probabilities are constructed based on diversity weights to select the feature direction. Then, the latent variable of the input image is transformed in the feature direction to the region beyond the hypersphere. Finally, the transformed latent variable is mapped to the pixel space based on the decoder to generate images outside the sampled data space to improve diversity. Experimentally, the proposed method considerably improves the performance of defect segmentation in different industrial scenes.