REDN: A Recursive Encoder-Decoder Network for Edge Detection.

Area of Science:

• Computer Vision
• Deep Learning
• Image Processing

Background:

• Edge detection is crucial for image understanding.
• Existing methods face challenges in accurately identifying fine details and complex boundaries.
• Recursive refinement and effective gradient flow are key to improving edge detection accuracy.

Purpose of the Study:

• To introduce a novel Recursive Encoder-Decoder Network (REDN) for enhanced edge detection in natural images.
• To leverage recursive neural networks and skip-connections for iterative edge refinement and improved detail preservation.
• To advance the state-of-the-art in natural image edge detection.

Main Methods:

• Developed REDN, integrating a Recursive Neural Network with an Encoder-Decoder architecture.
• Incorporated skip-connections to facilitate gradient propagation and utilize early-stage encoder details.
• Trained and evaluated the network on standard datasets: BSDS500, NYUD, and Pascal Context.

Main Results:

• REDN significantly improves edge detection performance compared to existing methods.
• Achieved state-of-the-art results on benchmark datasets using ODS F-measure, OIS F-measure, and AP metrics.
• Demonstrated effective iterative refinement of edges and preservation of fine image details.

Conclusions:

• REDN offers a powerful and effective approach for natural image edge detection.
• The proposed architecture successfully integrates recursion and skip-connections for superior performance.
• REDN represents a significant advancement in the field of computer vision and image analysis.