Semantic segmentation aims to robustly predict coherent class labels for entire regions of an image. It is a scene understanding task that powers real-world applications (e.g., autonomous navigation). One important application, the use of imagery for automated semantic understanding of pedestrian environments, provides remote mapping of accessibility features in street environments. This application (and others like it) require detailed geometric information of geographical objects. Semantic segmentation is a prerequisite for this task since it maps contiguous regions of the same class as single entities. Importantly, semantic segmentation uses like ours are not pixel-wise outcomes; however, most of their quantitative evaluation metrics (e.g., mean Intersection Over Union) are based on pixel-wise similarities to a ground-truth, which fails to emphasize over- and under-segmentation properties of a segmentation model. Here, we introduce a new metric to assess region-based over- and under-segmentation. We analyze and compare it to other metrics, demonstrating that the use of our metric lends greater explainability to semantic segmentation model performance in real-world applications.

中文翻译：

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重新思考语义分割评估以解释和选择模型

语义分割的目的是针对图像的整个区域鲁棒地预测连贯的类别标签。这是一个场景理解任务，可为现实世界的应用程序提供动力（例如，自主导航）。一个重要的应用程序是使用图像对行人环境进行自动语义理解，它提供了街道环境中可访问性功能的远程映射。此应用程序（以及其他类似的应用程序）需要地理对象的详细几何信息。语义分割是此任务的先决条件，因为它会将相同类别的连续区域映射为单个实体。重要的是，像我们这样的语义分割使用并不是按像素划分的结果；但是，他们的大多数定量评估指标（例如，联合的平均交集）都是基于与真实情况的像素相似性，这无法强调细分模型的过度细分和不足细分属性。在这里，我们引入了一种新的指标来评估基于区域的过度细分和不足细分。我们对它进行了分析并将其与其他指标进行比较，表明使用我们的指标可为真实应用程序中的语义细分模型性能提供更大的可解释性。