Learning-based edge detection has hereunto been strongly supervised with pixel-wise annotations which are tedious to obtain manually. We study the problem of self-training edge detection, leveraging the untapped wealth of large-scale unlabeled image datasets. We design a self-supervised framework with multi-layer regularization and self-teaching. In particular, we impose a consistency regularization which enforces the outputs from each of the multiple layers to be consistent for the input image and its perturbed counterpart. We adopt L0-smoothing as the 'perturbation' to encourage edge prediction lying on salient boundaries following the cluster assumption in self-supervised learning. Meanwhile, the network is trained with multi-layer supervision by pseudo labels which are initialized with Canny edges and then iteratively refined by the network as the training proceeds. The regularization and self-teaching together attain a good balance of precision and recall, leading to a significant performance boost over supervised methods, with lightweight refinement on the target dataset. Furthermore, our method demonstrates strong cross-dataset generality. For example, it attains 4.8% improvement for ODS and 5.8% for OIS when tested on the unseen BIPED dataset, compared to the state-of-the-art methods.

翻译：基于学习的边缘检测方法至今高度依赖像素级标注，而人工获取此类标注费时费力。本文研究自训练边缘检测问题，旨在利用海量无标注图像数据集中未被发掘的丰富信息。我们设计了一个融合多层正则化与自教学机制的自监督框架。具体而言，我们引入一致性正则化，强制输入图像与其扰动版本在多个网络层的输出保持一致。采用L0平滑作为"扰动"手段，遵循自监督学习中的聚类假设，促使边缘预测位于显著边界上。同时，网络通过伪标签进行多层监督训练——伪标签初始化为Canny边缘检测结果，并在训练过程中由网络迭代优化。正则化与自教学共同实现了精度与召回率的良好平衡，在目标数据集上仅需轻量化微调即可显著超越有监督方法。此外，本方法展现出强大的跨数据集泛化能力。例如，在未见过的BIPED数据集上，与最先进方法相比，本方法在ODS指标上提升4.8%，在OIS指标上提升5.8%。