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 this https URL

本研究提出了一种双向级联网络结构（BDCN），结合比例增强模块（SEM），以改善对象不同尺度上的边缘检测，并在多个数据集上验证其有效性。