Data Augmentation with Illumination Correction in Sematic Segmentation

Abstract

In the training process of the deep learning network, data augmentation can effectively improve accuracy. Presently, the commonly used data augmentation methods have been mainly divided into space transformation and color transformation. However, these methods do not take into account the effects of the image’s illumination factor. Under natural conditions, the color, angle, and strength of the light will cause the color of the object to change, which is likely to cause segmentation errors. In this article, we start from the angle of illumination and perform data augmentation on the image. By applying the illumination correction algorithm based on Retinex theory, we are able to enhance the data and achieved good results in the experiment. However, there still remains some problems within the current algorithms based on Retinex theory. Especially for images with uniform illumination conditions, which can be overexposed after processed by these algorithms. In response to this problem, we have improved the relevant algorithm. The improved algorithm further improves the accuracy of semantic segmentation on the original basis.