Abstract. Recent methods for boundary or edge detection built on
Deep Convolutional Neural Networks (CNNs) typically suffer from the
issue of predicted edges being thick and need post-processing to obtain
crisp boundaries. Highly imbalanced categories of boundary versus background in training data is one of main reasons for the above problem.
In this work, the aim is to make CNNs produce sharp boundaries without post-processing. We introduce a novel loss for boundary detection,
which is very effective for classifying imbalanced data and allows CNNs
to produce crisp boundaries. Moreover, we propose an end-to-end network which adopts the bottom-up/top-down architecture to tackle the
task. The proposed network effectively leverages hierarchical features and
produces pixel-accurate boundary mask, which is critical to reconstruct
the edge map. Our experiments illustrate that directly making crisp prediction not only promotes the visual results of CNNs, but also achieves
better results against the state-of-the-art on the BSDS500 dataset (ODS
F-score of .815) and the NYU Depth dataset (ODS F-score of .762).