Edge detection is the basis of many computer vision applications. State of the art predominantly relies on deep learning with two decisive factors: dataset content and network's architecture. Most of the publicly available datasets are not curated for edge detection tasks. Here, we offer a solution to this constraint. First, we argue that edges, contours and boundaries, despite their overlaps, are three distinct visual features requiring separate benchmark datasets. To this end, we present a new dataset of edges. Second, we propose a novel architecture, termed Dense Extreme Inception Network for Edge Detection (DexiNed), that can be trained from scratch without any pre-trained weights. DexiNed outperforms other algorithms in the presented dataset. It also generalizes well to other datasets without any fine-tuning. The higher quality of DexiNed is also perceptually evident thanks to the sharper and finer edges it outputs.
翻译：远程检测是许多计算机视觉应用的基础。 艺术状态主要依靠深层次学习, 有两个决定性因素: 数据集内容和网络结构。 大多数公开可用的数据集不是用于边缘检测任务的。 在这里, 我们提出一个解决方案。 首先, 我们争辩说, 边缘、 轮廓和界限, 尽管有重叠之处, 有三个不同的视觉特征, 需要不同的基准数据集。 为此, 我们提出了一个新的边缘数据集 。 其次, 我们提议建立一个新颖的架构, 叫做 电磁探测的“ ensense Expeption Network Network ” ( DexinNed), 可以在没有预先训练重量的情况下从零到零上训练。 解密了演示数据集中的其他算法。 它还在不作任何微调的情况下, 将其他数据集概括为好。 脱氧核的更高质量也明显可见, 因为它输出的边缘是锐利和细微的。
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