A Brief Analysis of the Holistically-Nested Edge Detector
Rafael Grompone von Gioi, Gregory Randall
Rafael Grompone von Gioi, and Gregory Randall, A Brief Analysis of the Holistically-Nested Edge Detector, Image Processing On Line, 12 (2022), pp. 369–377. https://doi.org/10.5201/ipol.2022.422

Communicated by J. Matías Di Martino
Demo edited by Rafael Grompone von Gioi


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!
The original source code is available here (last checked 2022/10/03).