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 build 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 filed are possible.

翻译：近年来，研究者提出了多种语义分割方法以预测场景中像素的标签。通常，我们通过测量区域预测误差或边界预测误差来比较不同方法，但目前尚无能够同时评估这两方面的直观评估指标。本文提出一种名为加权交并比（wIoU）的新型语义分割评估度量。首先，该方法基于边界距离图构建权重图，通过边界重要性因子实现对每个像素的加权评估。通过设定边界重要性因子，所提出的wIoU能够同时评估轮廓与区域。我们在包含33个场景的数据集上验证了wIoU的有效性，并证明了其灵活性。采用所提出的评估指标，我们期望语义分割领域能够实现更灵活、更直观的评估。