Abstract: Benefiting from convolutional neural networks (CNN), edge detection has surpassed human performance on several benchmark datasets. However, such algorithms cannot guarantee the crispness of edges and the accuracy of positioning. In order to obtain the target edge map that is refined and clear, suppressing background texture effectively, and locating accurately, a crisp edge detection algorithm with fusion of convolutional features (FCF) is proposed in this paper. The algorithm uses VGG16 as the backbone network for convolutional feature extraction, fuses the convolutional features at different stages after upsampling, and obtains a crisp edge map through the refine fusion block (RFB) designed in this paper. RFB uses multiple GroupNorm refine blocks (GRB) to refine the resulting edge map. In addition, to balance edge pixels and non-edge pixels, this paper also proposes a refine dice loss (RD) function. On the BSDS500 dataset, the method proposed in this paper improves the F-score (ODS) of deep edge detectors such as HED and RCF by 2.8% and 2.1%, respectively. When edge detection evaluation is performed without non-maximal suppression (NMS), the F-score (ODS) and F-score (OIS) reach 0.801 and 0.816, respectively, outperforming other algorithms.

Key words: crisp edge detection, fusion of convolutional features (FCF), refine dice loss (RD), convolutional neural networks (CNN)

摘要： 受益于卷积神经网络（CNN），边缘检测性能在多个基准数据集上都已经超过人类水平。但这类算法无法保证边缘的清晰性和定位的准确性。为获取细化清晰、有效抑制背景纹理、定位准确的目标边缘图，提出了一种融合卷积特征的清晰边缘检测算法（FCF）。该算法使用VGG16作为主干网络进行卷积特征提取，将不同阶段的卷积特征上采样后进行特征融合，并通过所设计的细化融合模块（RFB）获得清晰的边缘图。RFB使用多个归一化细化块（GRB）来细化得到的边缘图。此外，为平衡边缘像素和非边缘像素，还提出一个细化骰子损失函数（RD）。在BSDS500数据集上，所提方法将HED、RCF等深度边缘检测器的F-score（ODS）分别提高了2.8%和2.1%；当不使用非极大值抑制（NMS）进行边缘检测评估时，F-score（ODS）、F-score（OIS）分别达到0.801和0.816，超过了其他算法。

关键词: 清晰边缘检测, 融合卷积特征（FCF）, 细化骰子损失（RD）, 卷积神经网络（CNN）