Richer Convolutional Features for Edge Detection

TL;DR

This paper introduces a novel edge detection method using richer multiscale convolutional features from CNNs, achieving state-of-the-art accuracy while maintaining high processing speed.

Contribution

It is the first to utilize rich hierarchical convolutional features for edge detection, combining multilevel information for improved accuracy.

Findings

Achieves 0.811 ODS F-measure on BSDS500

Fast version reaches 0.806 ODS F-measure at 30 FPS

First to adopt rich convolutional features in edge detection

Abstract

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.