# LS-Net: Fast Single-Shot Line-Segment Detector

arXiv: https://arxiv.org/abs/1912.09532 · HTML: https://mlsanity.com/paper/1912.09532/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2019.

## Built on by (most cited first, 5 of 5)

- [PLGAN: Generative Adversarial Networks for Power-Line Segmentation in Aerial Images](https://mlsanity.com/paper/2204.07243/) (2022)
- [YOLinO: Generic Single Shot Polyline Detection in Real Time](https://mlsanity.com/paper/2103.14420/) (2021)
- [Automatic High Resolution Wire Segmentation and Removal](https://mlsanity.com/paper/2304.00221/) (2023)
- [Vision-Based Power Line Cables and Pylons Detection for Low Flying Aircraft](https://mlsanity.com/paper/2407.14352/) (2024)
- [UCorr: Wire Detection and Depth Estimation for Autonomous Drones](https://mlsanity.com/paper/2509.14989/) (2025)

## Abstract (arXiv)

In low-altitude Unmanned Aerial Vehicle (UAV) flights, power lines are considered as one of the most threatening hazards and one of the most difficult obstacles to avoid. In recent years, many vision-based techniques have been proposed to detect power lines to facilitate self-driving UAVs and automatic obstacle avoidance. However, most of the proposed methods are typically based on a common three-step approach: (i) edge detection, (ii) the Hough transform, and (iii) spurious line elimination based on power line constrains. These approaches not only are slow and inaccurate but also require a huge amount of effort in post-processing to distinguish between power lines and spurious lines. In this paper, we introduce LS-Net, a fast single-shot line-segment detector, and apply it to power line detection. The LS-Net is by design fully convolutional and consists of three modules: (i) a fully convolutional feature extractor, (ii) a classifier, and (iii) a line segment regressor. Due to the unavailability of large datasets with annotations of power lines, we render synthetic images of power lines using the Physically Based Rendering (PBR) approach and propose a series of effective data augmentation techniques to generate more training data. With a customized version of the VGG-16 network as the backbone, the proposed approach outperforms existing state-of-the-art approaches. In addition, the LS-Net can detect power lines in near real-time (20.4 FPS). This suggests that our proposed approach has a promising role in automatic obstacle avoidance and as a valuable component of self-driving UAVs, especially for automatic autonomous power line inspection.