Bi-Directional Cascade Network for Perceptual Edge Detection

TL;DR

This paper introduces a Bi-Directional Cascade Network (BDCN) that improves edge detection across multiple scales by supervising individual layers at their specific scales and enriching multi-scale features with a Scale Enhancement Module, achieving state-of-the-art results.

Contribution

The paper proposes a novel BDCN architecture with scale-specific supervision and a SEM for multi-scale feature extraction, enhancing edge detection performance and network efficiency.

Findings

Achieves 0.828 ODS Fmeasure on BSDS500, surpassing previous methods.

Enables scale-specific edge detection with fewer parameters.

Demonstrates effectiveness on multiple datasets.

Abstract

Exploiting multi-scale representations is critical to improve edge detection for objects at different scales. To extract edges at dramatically different scales, we propose a Bi-Directional Cascade Network (BDCN) structure, where an individual layer is supervised by labeled edges at its specific scale, rather than directly applying the same supervision to all CNN outputs. Furthermore, to enrich multi-scale representations learned by BDCN, we introduce a Scale Enhancement Module (SEM) which utilizes dilated convolution to generate multi-scale features, instead of using deeper CNNs or explicitly fusing multi-scale edge maps. These new approaches encourage the learning of multi-scale representations in different layers and detect edges that are well delineated by their scales. Learning scale dedicated layers also results in compact network with a fraction of parameters. We evaluate our method on three datasets, i.e., BSDS500, NYUDv2, and Multicue, and achieve ODS Fmeasure of 0.828, 1.3% higher than current state-of-the art on BSDS500. The code has been available at https://github.com/pkuCactus/BDCN.