Edge detection is a fundamental problem in computer vision. Recently, convolutional neural networks (CNNs) have pushed forward this field significantly. Existing methods which adopt specific layers of deep CNNs may fail to capture complex data structures caused by variations of scales and aspect ratios. In this paper, we propose an accurate edge detector using richer convolutional features (RCF). RCF encapsulates all convolutional features into more discriminative representation, which makes good usage of rich feature hierarchies, and is amenable to training via backpropagation. RCF fully exploits multiscale and multilevel information of objects to perform the image-to-image prediction holistically. Using VGG16 network, we achieve state-of-the-art performance on several available datasets. When evaluating on the well-known BSDS500 benchmark, we achieve ODS F-measure of 0.811 while retaining a fast speed (8 FPS). Besides, our fast version of RCF achieves ODS F-measure of 0.806 with 30 FPS. We also demonstrate the versatility of the proposed method by applying RCF edges for classical image segmentation.

中文翻译：

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丰富的边缘检测卷积功能

边缘检测是计算机视觉中的一个基本问题。最近，卷积神经网络（CNN）大大推动了这一领域的发展。采用深层CNN特定层的现有方法可能无法捕获由于比例尺和纵横比的变化而导致的复杂数据结构。在本文中，我们提出了一种使用更丰富的卷积特征（RCF）的精确边缘检测器。RCF将所有卷积特征封装为更具区分性的表示形式，从而很好地利用了丰富的特征层次结构，并且可以通过反向传播进行训练。RCF充分利用对象的多尺度和多层次信息来全面地执行图像到图像的预测。使用VGG16网络，我们可以在几个可用的数据集上实现最先进的性能。在以著名的BSDS500基准进行评估时，我们在保持快速（8 FPS）的同时实现了0.811的ODS F测度。此外，我们的RCF快速版本以30 FPS达到了0.806的ODS F测度。我们还通过应用RCF边缘进行经典图像分割来证明所提出方法的多功能性。