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In recent years, many semantic segmentation methods have been proposed topredict label of pixels in the scene. In general, we measure area predictionerrors or boundary prediction errors for comparing methods. However, there isno intuitive evaluation metric that evaluates both aspects. In this work, wepropose a new evaluation measure called weighted Intersection over Union (wIoU)for semantic segmentation. First, it build a weight map generated from aboundary distance map, allowing weighted evaluation for each pixel based on aboundary importance factor. The proposed wIoU can evaluate both contour andregion by setting a boundary importance factor. We validated the effectivenessof wIoU on a dataset of 33 scenes and demonstrated its flexibility. Using theproposed metric, we expect more flexible and intuitive evaluation in semanticsegmentation filed are possible.

Weighted Intersection over Union (wIoU): A New Evaluation Metric for Image Segmentation