Deep learning

Pattern recognition (psychology)

Operating system

Image (mathematics)

Data mining

Machine learning

Engineering

Enhanced Data Rates for GSM Evolution

Edge device

Artificial intelligence

Edge detection

Systems engineering

Measure (data warehouse)

Computer science

Task (project management)

Cloud computing

Process (computing)

Computer vision

Training set

Telecommunications

Image processing

Detector

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.

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