Data Augmentation Technique to Expand Road Dataset Using Mask RCNN and Image Inpainting

Abstract

A popular method for training a machine learning model is to use a data-driven approach. This research contributes to expanding the dataset of urban road images without vehicles. Using this method will benefit a variety of existing and new research projects that require an empty road to train their data-driven model. To achieve the desired result, the method combines image segmentation and image inpainting. The model detects the vehicle with Mask RCNN and removes the detected object with image inpainting. Morphological transformation was used to improve the method's efficiency. Using dilation operation of morphological transformation, the mask generated from mask RCNN is enlarged. The results of the experiment support the method's efficacy.