  Significant progress has been made in boundary detection with the help of
convolutional neural networks. Recent boundary detection models not only focus
on real object boundary detection but also "crisp" boundaries (precisely
localized along the object's contour). There are two methods to evaluate crisp
boundary performance. One uses more strict tolerance to measure the distance
between the ground truth and the detected contour. The other focuses on
evaluating the contour map without any postprocessing. In this study, we
analyze both methods and conclude that both methods are two aspects of crisp
contour evaluation. Accordingly, we propose a novel network named deep
refinement network (DRNet) that stacks multiple refinement modules to achieve
richer feature representation and a novel loss function, which combines
cross-entropy and dice loss through effective adaptive fusion. Experimental
results demonstrated that we achieve state-of-the-art performance for several
available datasets.
