  We present a new version of YOLO with better performance and extended with
instance segmentation called Poly-YOLO. Poly-YOLO builds on the original ideas
of YOLOv3 and removes two of its weaknesses: a large amount of rewritten labels
and inefficient distribution of anchors. Poly-YOLO reduces the issues by
aggregating features from a light SE-Darknet-53 backbone with a hypercolumn
technique, using stairstep upsampling, and produces a single scale output with
high resolution. In comparison with YOLOv3, Poly-YOLO has only 60% of its
trainable parameters but improves mAP by a relative 40%. We also present
Poly-YOLO lite with fewer parameters and a lower output resolution. It has the
same precision as YOLOv3, but it is three times smaller and twice as fast, thus
suitable for embedded devices. Finally, Poly-YOLO performs instance
segmentation using bounding polygons. The network is trained to detect
size-independent polygons defined on a polar grid. Vertices of each polygon are
being predicted with their confidence, and therefore Poly-YOLO produces
polygons with a varying number of vertices.
