Dense Extreme Inception Network for Edge Detection

• URL: http://arxiv.org/abs/2112.02250v1
• Date: Sat, 4 Dec 2021 05:38:50 GMT
• Title: Dense Extreme Inception Network for Edge Detection
• Authors: Xavier Soria Poma, Angel Sappa, Patricio Humanante, Arash Arbarinia
• Abstract summary: Edge detection is the basis of many computer vision applications. Most of the publicly available datasets are not curated for edge detection tasks. We present a new dataset of edges. We propose a novel architecture, termed Dense Extreme Inception Network for Edge Detection (DexiNed)
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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