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

Data augmentation is a common technique to increase dataset. This study proposes a method that generates image data of empty road dataset. Data-driven approach is a popular approach to training an intelligent model. Various existing researches or new researches that require the empty road to train their data-driven model will be benefited by using this method. The method uses image inpainting to remove vehicles from input images. The model detects the vehicle using Mask RCNN and using image inpainting the model removes the detected object. To increase the efficiency of the method morphological transformation was used. The morphological transformation is also adjusted depending on the output and may need several iterations to ensure better results. The experiment results uphold the efficiency of the method.