Learning-based edge detection usually suffers from predicting thick edges. Through extensive quantitative study with a new edge crispness measure, we find that noisy human-labeled edges are the main cause of thick predictions. Based on this observation, we advocate that more attention should be paid on label quality than on model design to achieve crisp edge detection. To this end, we propose an effective Canny-guided refinement of human-labeled edges whose result can be used to train crisp edge detectors. Essentially, it seeks for a subset of over-detected Canny edges that best align human labels. We show that several existing edge detectors can be turned into a crisp edge detector through training on our refined edge maps. Experiments demonstrate that deep models trained with refined edges achieve significant performance boost of crispness from 17.4% to 30.6%. With the PiDiNet backbone, our method improves ODS and OIS by 12.2% and 12.6% on the Multicue dataset, respectively, without relying on non-maximal suppression. We further conduct experiments and show the superiority of our crisp edge detection for optical flow estimation and image segmentation.

中文翻译：

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深入探讨清晰度：用于清晰度边缘检测的引导标签细化

基于学习的边缘检测通常会遇到预测厚边缘的问题。通过使用新的边缘清晰度度量进行广泛的定量研究，我们发现嘈杂的人类标记边缘是粗预测的主要原因。基于这一观察，我们主张应该更多地关注标签质量而不是模型设计，以实现清晰的边缘检测。为此，我们提出了一种有效的 Canny 引导的人类标记边缘细化方法，其结果可用于训练清晰的边缘检测器。本质上，它寻找过度检测的 Canny 边缘的子集，以最好地对齐人类标签。我们表明，通过对我们的精细边缘图进行训练，可以将几个现有的边缘检测器转变为清晰的边缘检测器。实验表明，经过精细边缘训练的深度模型可将清晰度显着提高，从 17.4% 提高到 30.6%。借助 PiDiNet 主干，我们的方法在 Multicue 数据集上分别将 ODS 和 OIS 提高了 12.2% 和 12.6%，而不依赖于非极大值抑制。我们进一步进行实验并展示了我们的清晰边缘检测对于光流估计和图像分割的优越性。