  Instance segmentation has gained recently huge attention in various computer
vision applications. It aims at providing different IDs to different objects of
the scene, even if they belong to the same class. Instance segmentation is
usually performed as a two-stage pipeline. First, an object is detected, then
semantic segmentation within the detected box area is performed which involves
costly up-sampling. In this paper, we propose Insta-YOLO, a novel one-stage
end-to-end deep learning model for real-time instance segmentation. Instead of
pixel-wise prediction, our model predicts instances as object contours
represented by 2D points in Cartesian space. We evaluate our model on three
datasets, namely, Carvana,Cityscapes and Airbus. We compare our results to the
state-of-the-art models for instance segmentation. The results show our model
achieves competitive accuracy in terms of mAP at twice the speed on GTX-1080
GPU.
