VEHICLE INSTANCE SEGMENTATION WITH ROTATED BOUNDING BOXES IN UAV IMAGES USING CNN

Abstract. Vehicle instance segmentation is a major but challenging task in aerial remote sensing applications. More importantly, the current majority methods use horizontal bounding boxes which does not tell much about the orientation of vehicles and often leads to inaccurate mask proposals due to high background to foreground pixel-ratio. Given that the orientation of vehicles is important for numerous applications like vehicle tracking, we introduce in this paper a deep neural network to detect and segment vehicles using rotated bounding boxes in aerial images. Our method demonstrates that rotated instance segmentation improves the mask predictions, especially when objects are not axis aligned or are touching. We evaluate our model on the ISPRS benchmark dataset and our newly introduced UAV dataset for vehicle segmentation and show that we can significantly improve the mask accuracy compared to instance segmentation using axis-aligned bounding boxes.