密集的极端起始网络：建立用于边缘检测的鲁棒的CNN模型

本文提出了一种基于深度学习的边缘检测器，该检测器受HED（整体嵌套边缘检测）和Xception网络的启发。所提出的方法生成了细的边缘图，这对于人眼是合理的。它可以用于任何边缘检测任务，而无需事先培训或微调过程。..

Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection

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.