# Line detection via a lightweight CNN with a Hough Layer

arXiv: https://arxiv.org/abs/2008.08884 · HTML: https://mlsanity.com/paper/2008.08884/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [LSDNet: Trainable Modification of LSD Algorithm for Real-Time Line Segment Detection](https://mlsanity.com/paper/2209.04642/) (2022)
- [A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges](https://mlsanity.com/paper/2305.00264/) (2023)
- [Fast matrix multiplication for binary and ternary CNNs on ARM CPU](https://mlsanity.com/paper/2205.09120/) (2022)
- [STARLINC: Satellite Trail Artifact Removal using Inter-Frame Correlation](https://mlsanity.com/paper/2608.29145/) (2026)

## Abstract (arXiv)

Line detection is an important computer vision task traditionally solved by Hough Transform. With the advance of deep learning, however, trainable approaches to line detection became popular. In this paper we propose a lightweight CNN for line detection with an embedded parameter-free Hough layer, which allows the network neurons to have global strip-like receptive fields. We argue that traditional convolutional networks have two inherent problems when applied to the task of line detection and show how insertion of a Hough layer into the network solves them. Additionally, we point out some major inconsistencies in the current datasets used for line detection.