# Towards Light-weight and Real-time Line Segment Detection

arXiv: https://arxiv.org/abs/2106.00186 · HTML: https://mlsanity.com/paper/2106.00186/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 45 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 29 of 29)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Line Segment Detection Using Transformers without Edges](https://mlsanity.com/paper/2101.01909/) (2021)
- [End-to-End Wireframe Parsing](https://mlsanity.com/paper/1905.03246/) (2019)
- [Learning to Parse Wireframes in Images of Man-Made Environments](https://mlsanity.com/paper/2007.07527/) (2020)
- [Holistically-Attracted Wireframe Parsing](https://mlsanity.com/paper/2003.01663/) (2020)
- [Pelee: A Real-Time Object Detection System on Mobile Devices](https://mlsanity.com/paper/1804.06882/) (2018)
- [Learning Attraction Field Representation for Robust Line Segment Detection](https://mlsanity.com/paper/1812.02122/) (2018)
- [Deep Hough-Transform Line Priors](https://mlsanity.com/paper/2007.09493/) (2020)
- [PPGNet: Learning Point-Pair Graph for Line Segment Detection](https://mlsanity.com/paper/1905.03415/) (2019)
- [TP-LSD: Tri-Points Based Line Segment Detector](https://mlsanity.com/paper/2009.05505/) (2020)
- [Learning to Calibrate Straight Lines for Fisheye Image Rectification](https://mlsanity.com/paper/1904.09856/) (2019)
- [Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages](https://mlsanity.com/paper/1807.11013/) (2018)
- [LGNN: A Context-aware Line Segment Detector](https://mlsanity.com/paper/2008.05892/) (2020)
- [Camera Pose Estimation from Lines using Pl"ucker Coordinates](https://mlsanity.com/paper/1608.02824/) (2016)
- [Anisotropic-Scale Junction Detection and Matching for Indoor Images](https://mlsanity.com/paper/1703.05630/) (2017)
- [MCMLSD: A Probabilistic Algorithm and Evaluation Framework for Line Segment Detection](https://mlsanity.com/paper/2001.01788/) (2020)
- [Absolute Pose Estimation from Line Correspondences using Direct Linear Transformation](https://mlsanity.com/paper/1608.06891/) (2016)

## Built on by (most cited first, 30 of 33)

- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models](https://mlsanity.com/paper/2303.04671/) (2023)
- [Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2305.16322/) (2023)
- [DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients](https://mlsanity.com/paper/2212.07766/) (2022)
- [VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning](https://mlsanity.com/paper/2504.07960/) (2025)
- [Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning](https://mlsanity.com/paper/2210.12971/) (2022)
- [OmniControlNet: Dual-stage Integration for Conditional Image Generation](https://mlsanity.com/paper/2406.05871/) (2024)
- [Chat-Edit-3D: Interactive 3D Scene Editing via Text Prompts](https://mlsanity.com/paper/2407.06842/) (2024)
- [The Potential of Visual ChatGPT For Remote Sensing](https://mlsanity.com/paper/2304.13009/) (2023)
- [Why Only Text: Empowering Vision-and-Language Navigation with Multi-modal Prompts](https://mlsanity.com/paper/2406.02208/) (2024)
- [A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges](https://mlsanity.com/paper/2305.00264/) (2023)
- [FineControlNet: Fine-level Text Control for Image Generation with Spatially Aligned Text Control Injection](https://mlsanity.com/paper/2312.09252/) (2023)
- [When ControlNet Meets Inexplicit Masks: A Case Study of ControlNet on its Contour-following Ability](https://mlsanity.com/paper/2403.00467/) (2024)
- [Isolated Diffusion: Optimizing Multi-Concept Text-to-Image Generation Training-Freely with Isolated Diffusion Guidance](https://mlsanity.com/paper/2403.16954/) (2024)
- [FlexEControl: Flexible and Efficient Multimodal Control for Text-to-Image Generation](https://mlsanity.com/paper/2405.04834/) (2024)
- [ScaleLSD: Scalable Deep Line Segment Detection Streamlined](https://mlsanity.com/paper/2506.09369/) (2025)
- [SIG: A Synthetic Identity Generation Pipeline for Generating Evaluation Datasets for Face Recognition](https://mlsanity.com/paper/2409.08345/) (2024)
- [Synthesizing Realistic Data for Table Recognition](https://mlsanity.com/paper/2404.11100/) (2024)
- [Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation](https://mlsanity.com/paper/2501.09194/) (2025)
- [Lattice-allocated Real-time Line Segment Feature Detection and Tracking Using Only an Event-based Camera](https://mlsanity.com/paper/2510.06829/) (2025)
- [Level-line Guided Edge Drawing for Robust Line Segment Detection](https://mlsanity.com/paper/2305.05883/) (2023)
- [Improving Transformer Based Line Segment Detection with Matched Predicting and Re-ranking](https://mlsanity.com/paper/2502.17766/) (2025)
- [Artificial Satellite Trails Detection Using U-Net Deep Neural Network and Line Segment Detector Algorithm](https://mlsanity.com/paper/2509.16771/) (2025)
- [MiLSD: A Micro Line-Segment Detector for Resource-Constrained Devices](https://mlsanity.com/paper/2607.06600/) (2026)
- [Unlocking the Potential of Image Editing via Concept Scaling and Dense Supervision](https://mlsanity.com/paper/2608.16812/) (2026)
- [Chat-Edit-3D++: Interactive 3D and 4D Scene Editing via Large Language Models](https://mlsanity.com/paper/2608.29137/) (2026)
- [Staircase Localization for Autonomous Exploration in Urban Environments](https://mlsanity.com/paper/2403.17330/) (2024)
- [Linear Anchored Gaussian Mixture Model for Location and Width Computations of Objects in Thick Line Shape](https://mlsanity.com/paper/2404.03043/) (2024)
- [Optimizing Multi-Task Learning for Accurate Spacecraft Pose Estimation](https://mlsanity.com/paper/2410.12679/) (2024)
- [Adaptive Dual-Constrained Line Aggregation for Cross-Paradigm Line Segment Detection](https://mlsanity.com/paper/2508.19742/) (2025)

## Abstract (arXiv)

Previous deep learning-based line segment detection (LSD) suffers from the immense model size and high computational cost for line prediction. This constrains them from real-time inference on computationally restricted environments. In this paper, we propose a real-time and light-weight line segment detector for resource-constrained environments named Mobile LSD (M-LSD). We design an extremely efficient LSD architecture by minimizing the backbone network and removing the typical multi-module process for line prediction found in previous methods. To maintain competitive performance with a light-weight network, we present novel training schemes: Segments of Line segment (SoL) augmentation, matching and geometric loss. SoL augmentation splits a line segment into multiple subparts, which are used to provide auxiliary line data during the training process. Moreover, the matching and geometric loss allow a model to capture additional geometric cues. Compared with TP-LSD-Lite, previously the best real-time LSD method, our model (M-LSD-tiny) achieves competitive performance with 2.5% of model size and an increase of 130.5% in inference speed on GPU. Furthermore, our model runs at 56.8 FPS and 48.6 FPS on the latest Android and iPhone mobile devices, respectively. To the best of our knowledge, this is the first real-time deep LSD available on mobile devices. Our code is available.