  In this paper, we introduce RED-NET: A Recursive Encoder-Decoder Network with
Skip-Connections for edge detection in natural images. The proposed network is
a novel integration of a Recursive Neural Network with an Encoder-Decoder
architecture. The recursive network enables us to increase the network depth
without increasing the number of parameters. Adding skip-connections between
encoder and decoder helps the gradients reach all the layers of a network more
easily and allows information related to finer details in the early stage of
the encoder to be fully utilized in the decoder. Based on our extensive
experiments on popular boundary detection datasets including BSDS500
\cite{Arbelaez2011}, NYUD \cite{Silberman2012} and Pascal Context
\cite{Mottaghi2014}, RED-NET significantly advances the state-of-the-art on
edge detection regarding standard evaluation metrics such as Optimal Dataset
Scale (ODS) F-measure, Optimal Image Scale (OIS) F-measure, and Average
Precision (AP).
