Image Edge detection (ED) is a base task in computer vision. While the performance of the ED algorithm has been improved greatly by introducing CNN-based models, current models still suffer from unsatisfactory precision rates especially when only a low error toleration distance is allowed. Therefore, model architecture for more precise predictions still needs an investigation. On the other hand, the unavoidable noise training data provided by humans would lead to unsatisfactory model predictions even when inputs are edge maps themselves, which also needs improvement. In this paper, more precise ED models are presented with cascaded skipping density blocks (CSDB). Our models obtain state-of-the-art(SOTA) predictions in several datasets, especially in average precision rate (AP), which is confirmed by extensive experiments. Moreover, our models do not include down-sample operations, demonstrating those widely believed operations are not necessary. Also, a novel modification on data augmentation for training is employed, which allows noiseless data to be employed in model training and thus improves the performance of models predicting on edge maps themselves.

翻译：图像边缘检测是计算机视觉中的一项基础任务。尽管通过引入基于CNN的模型，边缘检测算法的性能已得到显著提升，但现有模型在仅允许较小误差容忍距离时，其精确率仍不尽如人意。因此，实现更高精度预测的模型架构仍需深入研究。另一方面，人工标注不可避免的噪声训练数据会导致模型预测效果不佳，即使输入为边缘图本身时亦是如此，这也需要改进。本文提出采用级联跳跃密度块的更精确边缘检测模型。我们的模型在多个数据集上实现了最先进的预测性能，尤其在平均精确率指标上表现突出，这已通过大量实验验证。此外，我们的模型不包含下采样操作，证明这些被广泛认为必要的操作并非必需。同时，我们采用了一种创新的训练数据增强改进方法，使得无噪声数据能够用于模型训练，从而提升了模型在边缘图自身预测上的性能表现。