Dexined (edge detection)

Dense Extreme Inception Network for Edge Detection

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.

Tasks: Edge Detection

Datasets: BSD500, BIPED, MDBD

Journal: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)

Licence: MIT

https://github.com/xavysp/DexiNed/raw/master/figs/DexiNed_banner.png