Learning to utilize image second-order derivative information for crisp edge detection

TL;DR

This paper introduces a novel U-shape network called LUS-Net that leverages second-order derivative information, multi-scale context, and boundary refinement modules to achieve state-of-the-art, crisp edge detection results.

Contribution

It proposes a second-order derivative-based multi-scale module, a hybrid focal loss, and a boundary refinement module within a U-shape architecture for improved edge detection.

Findings

Achieves state-of-the-art results on BSDS500, NYUD-V2, and BIPED datasets.

Produces crisp, clean, and accurate edge maps.

Outperforms existing methods in edge detection benchmarks.

Abstract

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).