Bi-Directional Cascade Network for Perceptual Edge Detection

Jianzhong He, Shiliang Zhang, Ming Yang, Yanhu Shan, Tiejun Huang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 3828-3837

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.

Related Material


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[bibtex]
@InProceedings{He_2019_CVPR,
author = {He, Jianzhong and Zhang, Shiliang and Yang, Ming and Shan, Yanhu and Huang, Tiejun},
title = {Bi-Directional Cascade Network for Perceptual Edge Detection},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}