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.
@article{arxiv.1909.01955,
title = {Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection},
author = {Xavier Soria and Edgar Riba and Angel D. Sappa},
journal= {arXiv preprint arXiv:1909.01955},
year = {2020}
}