出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Object detection typically requires a large amount of data to ensure detection accuracy.
However, it is often impossible to ensure sufficient data in practice. This paper presents a new
data augmentation method based on pixel-level image blend and domain adaptation. This
method consists of two steps: 1.Image blend using a labeled dataset as object instances and an
unlabeled dataset as background images.2. Domain adaptation based on Cycle Generative
Adversarial Networks (Cycle GAN).A neural network will be trained to transform samples from
step 1 to approximate the original dataset. Statistical consistency between new dataset
generated by different data augmentation methods and original dataset will be measured by
metrics such as generator loss and hellinger distance. Furthermore, a detection/segmentation
network for diabetic retinopathy based on Mask R-CNN will be built and trained by the
generated dataset. The effect of data augmentation method on the detection accuracy will be
presented.
