  Predicting a binary mask for an object is more accurate but also more
computationally expensive than a bounding box. Polygonal masks as developed in
CenterPoly can be a good compromise. In this paper, we improve over CenterPoly
by enhancing the classical regression L1 loss with a novel region-based loss
and a novel order loss, as well as with a new training process for the vertices
prediction head. Moreover, the previous methods that predict polygonal masks
use different coordinate systems, but it is not clear if one is better than
another, if we abstract the architecture requirement. We therefore investigate
their impact on the prediction. We also use a new evaluation protocol with
oracle predictions for the detection head, to further isolate the segmentation
process and better compare the polygonal masks with binary masks. Our instance
segmentation method is trained and tested with challenging datasets containing
urban scenes, with a high density of road users. Experiments show, in
particular, that using a combination of a regression loss and a region-based
loss allows significant improvements on the Cityscapes and IDD test set
compared to CenterPoly. Moreover the inference stage remains fast enough to
reach real-time performance with an average of 0.045 s per frame for
2048$\times$1024 images on a single RTX 2070 GPU. The code is available
$\href{https://github.com/KatiaJDL/CenterPoly-v2}{\text{here}}$.
