Data Augmentation Methods for Semantic Segmentation-based Mobile Robot Perception System

Abstract

Data augmentation has become a standard technique for increasing deep learning models’ accuracy and robustness. Different pixel intensity modifications, image transformations, and noise additions represent the most utilized data augmentation methods. In this paper, a comprehensive evaluation of data augmentation techniques for mobile robot perception system is performed. The perception system based on a deep learning model for semantic segmentation is augmented by 17 techniques to obtain better generalization characteristics during the training process. The deep learning model is trained and tested on custom dataset and utilized in real-time scenarios. The experimental results show the increment of 6.2 in mIoU for the best combination of data augmentation strategies.