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arXiv · 2112.02250

Dense Extreme Inception Network for Edge Detection

Xavier Soria·Angel Sappa·Patricio Humanante·Arash Akbarinia
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Abstract

<< >>. 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.

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Xavier Soria, Angel Sappa, Patricio Humanante, Arash Akbarinia. 2021-12-04. Dense Extreme Inception Network for Edge Detection. https://doi.org/10.1016/j.patcog.2023.109461

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