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

Code

• github.com/xavysp/DexiNed
  OfficialIn papertf★ 843
• github.com/a-nau/Plane-Segmentation-Refinement
  none★ 51
• github.com/xavysp/MBIPED
  none★ 0
• github.com/chaitravi-ce/Edge-Detection-Using-ML
  none★ 0

Abstract

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.

Tasks

Benchmark Results

Reproductions