  Edge detection is a fundamental task in computer vision. It has made great
progress under the development of deep convolutional neural networks (DCNNs),
some of which have achieved a beyond human-level performance. However, recent
top-performing edge detection methods tend to generate thick and noisy edge
lines. In this work, we solve this problem from two aspects: (1) the lack of
prior knowledge regarding image edges, and (2) the issue of imbalanced pixel
distribution. We propose a second-order derivative-based multi-scale contextual
enhancement module (SDMCM) to help the model locate true edge pixels accurately
by introducing the edge prior knowledge. We also construct a hybrid focal loss
function (HFL) to alleviate the imbalanced distribution issue. In addition, we
employ the conditionally parameterized convolution (CondConv) to develop a
novel boundary refinement module (BRM), which can further refine the final
output edge maps. In the end, we propose a U-shape network named LUS-Net which
is based on the SDMCM and BRM for crisp edge detection. We perform extensive
experiments on three standard benchmarks, and the experiment results illustrate
that our method can predict crisp and clean edge maps and achieves
state-of-the-art performance on the BSDS500 dataset (ODS=0.829), NYUD-V2
dataset (ODS=0.768), and BIPED dataset (ODS=0.903).
