Edge detection is a fundamental task in computer vision and it has made great
progress under the development of deep convolutional neural networks (DCNNs),
some of them have achieved a beyond human-level performance. However, recent
top-performing edge detection methods tend to generate thick and blurred edge
lines. In this work, we propose an effective method to solve this problem. Our
approach consists of a lightweight pre-trained backbone, multi-scale contextual
enhancement module aggregating gradient information (MCGI), boundary correction
module (BCM), and boundary refinement module (BRM). In addition to this, we
construct a novel hybrid loss function based on the Tversky index for solving
the issue of imbalanced pixel distribution. We test our method on three
standard benchmarks and the experiment results illustrate that our method
improves the visual effect of edge maps and achieves a top performance among
several state-of-the-art methods on the BSDS500 dataset (ODS F-score in
standard evaluation is 0.829, in crispness evaluation is 0.720), NYUD-V2
dataset (ODS F-score in standard evaluation is 0.768, in crispness evaluation
is \textbf{0.546}), and BIPED dataset (ODS F-score in standard evaluation is
0.903).

本文介绍了一种有效解决边缘检测中生成粗糙模糊边缘线的方法，该方法利用轻量级预训练主干网络、多尺度上下文信息聚合模块 (MCGI)、边界校正模块 (BCM) 和边界优化模块 (BRM) 来改善边缘图像的视觉效果，并通过基于 Tversky 指数的混合损失函数解决像素分布不平衡的问题。在三个标准基准数据集上进行实验，结果表明该方法在 BSDS500、NYUD-V2 和 BIPED 数据集上均取得了最佳性能。