Richer Convolutional Features for Edge Detection

In this paper, we propose an accurate edge detector using richer convolutional features (RCF). Since objects in natural images possess various scales and aspect ratios, learning the rich hierarchical representations is very critical for edge detection. CNNs have been proved to be effective for this task. In addition, the convolutional features in CNNs gradually become coarser with the increase of the receptive fields. According to these observations, we attempt to adopt richer convolutional features in such a challenging vision task. The proposed network fully exploits multiscale and multilevel information of objects to perform the image-to-image prediction by combining all the meaningful convolutional…

• NN
  National Natural Science Foundation of China

  Awards: 61572264, 61620106008
• NS
  Natural Science Foundation of Tianjin City

  Award: 17JCJQJC43700