  In this paper, we propose an accurate edge detector using richer
convolutional features (RCF). Since objects in nature images have various
scales and aspect ratios, the automatically learned rich hierarchical
representations by CNNs are very critical and effective to detect edges and
object boundaries. And the convolutional features gradually become coarser with
receptive fields increasing. Based on these observations, our proposed network
architecture makes full use of multiscale and multi-level information to
perform the image-to-image edge prediction by combining all of the useful
convolutional features into a holistic framework. It is the first attempt to
adopt such rich convolutional features in computer vision tasks. Using VGG16
network, we achieve \sArt results on several available datasets. When
evaluating on the well-known BSDS500 benchmark, we achieve ODS F-measure of
\textbf{.811} while retaining a fast speed (\textbf{8} FPS). Besides, our fast
version of RCF achieves ODS F-measure of \textbf{.806} with \textbf{30} FPS.
