Delving into Crispness: Guided Label Refinement for Crisp Edge Detection
Yunfan Ye Renjiao Yi Zhirui Gao Zhiping Cai Kai Xu
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.