Abstract In 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 it 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 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. 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.

Van Nhan Nguyen

Robert Jenssen

Davide Roverso

Machine Vision and Applications

机器视觉和应用程序发布机器视觉研究和开发方面的高质量技术贡献。具体来说，编辑鼓励在图像相关计算的所有应用和工程方面提交。特别是涉及机器视觉的科学、商业、工业、军事和生物医学应用的原创性贡献都在该期刊的范围内。特别强调图像处理和计算机视觉的工程和技术方面。以下方面机器视觉应用的兴趣：算法、架构、VLSI 实现、人工智能技术和机器视觉专家系统、前端传感、多维和多传感器机器视觉、实时技术、图像数据库、虚拟现实和可视化。论文必须包含重要的实验验证部分。

The journal "Machine Vision and Applications" publishes high-quality technical contributions in the field of machine vision research and development. Specifically, we encourage submissions related to all applications and engineering aspects of image-related computing. Original contributions involving scientific, commercial, industrial, military, and biomedical applications of machine vision are particularly welcome. The journal emphasizes the engineering and technical aspects of image processing and computer vision. We are especially interested in the following areas of machine vision applications: algorithms, architectures, VLSI implementations, artificial intelligence technologies and machine vision expert systems, front-end sensing, multi-dimensional and multi-sensor machine vision, real-time technologies, image databases, virtual reality, and visualization. All submitted papers must include significant experimental validation.

《Machine Vision and Applications》期刊致力于发布机器视觉领域的高质量技术研究和开发成果。我们鼓励提交与图像相关计算的各类应用和工程研究，尤其是涉及科学、商业、工业、军事和生物医学等领域的原创性贡献。期刊特别强调图像处理和计算机视觉的工程及技术方面。我们对以下机器视觉应用领域特别感兴趣：算法、架构、VLSI实现、人工智能技术和机器视觉专家系统、前端传感器、多维和多传感器机器视觉、实时技术、图像数据库、虚拟现实和可视化。所有提交的论文必须包含重要的实验验证部分。

LS-Net: fast single-shot line-segment detector

The LS-Net, a fast single-shot line-segment detector, is introduced and it can detect power lines in near real time, suggesting that the 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.

Category	Quartile
计算机科学	4
计算机科学, 计算机人工智能	4
计算机科学, 计算机控制论	4
计算机科学, 工程电子与电气	4

LS-Net: fast single-shot line-segment detector

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Journal Info

Category	Quartile
ENGINEERING, ELECTRICAL & ELECTRONIC	2