  In recent years, many semantic segmentation methods have been proposed to
predict label of pixels in the scene. In general, we measure area prediction
errors or boundary prediction errors for comparing methods. However, there is
no intuitive evaluation metric that evaluates both aspects. In this work, we
propose a new evaluation measure called weighted Intersection over Union (wIoU)
for semantic segmentation. First, it builds a weight map generated from a
boundary distance map, allowing weighted evaluation for each pixel based on a
boundary importance factor. The proposed wIoU can evaluate both contour and
region by setting a boundary importance factor. We validated the effectiveness
of wIoU on a dataset of 33 scenes and demonstrated its flexibility. Using the
proposed metric, we expect more flexible and intuitive evaluation in semantic
segmentation field are possible.
