A Brief Analysis of the Holistically-Nested Edge Detector

Rafael Grompone von Gioi, Gregory Randall

Abstract: This work describes the HED method for edge detection. HED uses a neural network based on a VGG16 backbone, supplemented with some extra layers for merging the results at different scales. The training was performed on an augmented version of the BSDS500 dataset. We perform a brief analysis of the results produced by HED, highlighting its quality but also indicating its limitations. Overall, HED produces state-of-the-art results. **This is an MLBriefs article, the source code has not been reviewed!**<br> **The original source code is [[available here|https://github.com/sniklaus/pytorch-hed/tree/8db09037f2491abbed4e5d7e37ec75210a2dbfbf]] (last checked 2022/10/03).**