Instance Segmentation and Object Detection with Bounding Shape Masks

Many recent object detection algorithms use the bounding box regressor to predict the position coordinates of an object (i.e., to predict four continuous variables of an object's bounding box information). To improve object detection accuracy, we propose four types of object boundary segmentation masks that provide position information in a different manner than that done by object detection algorithms, Additionally, we investigated the effect of the proposed object bounding shape masks on instance segmentation. To evaluate the proposed masks, our method adds a proposed bounding shape (or box) mask to extend the Faster R-CNN framework; we call this Bounding Shape (or Box) Mask R-CNN. We experimentally verified its performance with two benchmark datasets, MS COCO and Cityscapes. The results indicate that our proposed models generally outperform Faster R-CNN and Mask R-CNN.