# FINED: Fast Inference Network for Edge Detection

arXiv: https://arxiv.org/abs/2012.08392 · HTML: https://mlsanity.com/paper/2012.08392/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2020.

## Built on by (most cited first, 9 of 9)

- [Pixel Difference Networks for Efficient Edge Detection](https://mlsanity.com/paper/2108.07009/) (2021)
- [Lightweight Pixel Difference Networks for Efficient Visual Representation Learning](https://mlsanity.com/paper/2402.00422/) (2024)
- [Delving into Crispness: Guided Label Refinement for Crisp Edge Detection](https://mlsanity.com/paper/2306.15172/) (2023)
- [STEdge: Self-training Edge Detection with Multi-layer Teaching and Regularization](https://mlsanity.com/paper/2201.05121/) (2022)
- [A new baseline for edge detection: Make Encoder-Decoder great again](https://mlsanity.com/paper/2409.14976/) (2024)
- [A Robust Morphological Approach for Semantic Segmentation of Very High Resolution Images](https://mlsanity.com/paper/2208.01254/) (2022)
- [UHNet: An Ultra-Lightweight and High-Speed Edge Detection Network](https://mlsanity.com/paper/2408.04258/) (2024)
- [Training-free Quantum-Inspired Image Edge Extraction Method](https://mlsanity.com/paper/2501.18929/) (2025)
- [Hybrid Multi-Stage Learning Framework for Edge Detection: A Survey](https://mlsanity.com/paper/2503.21827/) (2025)

## Abstract (arXiv)

In this paper, we address the design of lightweight deep learning-based edge detection. The deep learning technology offers a significant improvement on the edge detection accuracy. However, typical neural network designs have very high model complexity, which prevents it from practical usage. In contrast, we propose a Fast Inference Network for Edge Detection (FINED), which is a lightweight neural net dedicated to edge detection. By carefully choosing proper components for edge detection purpose, we can achieve the state-of-the-art accuracy in edge detection while significantly reducing its complexity. Another key contribution in increasing the inferencing speed is introducing the training helper concept. The extra subnetworks (training helper) are employed in training but not used in inferencing. It can further reduce the model complexity and yet maintain the same level of accuracy. Our experiments show that our systems outperform all the current edge detectors at about the same model (parameter) size.