  This paper proposes a Deep Learning based edge detector, which is inspired on
both HED (Holistically-Nested Edge Detection) and Xception networks. The
proposed approach generates thin edge-maps that are plausible for human eyes;
it can be used in any edge detection task without previous training or fine
tuning process. As a second contribution, a large dataset with carefully
annotated edges has been generated. This dataset has been used for training the
proposed approach as well the state-of-the-art algorithms for comparisons.
Quantitative and qualitative evaluations have been performed on different
benchmarks showing improvements with the proposed method when F-measure of ODS
and OIS are considered.
