用3D视觉和机器学习自动检测电网部件，提升巡检效率与安全性。
Enhancing Power Grid Inspections with Machine Learning
- 基于3D LiDAR点云与变换器模型实现电网组件语义分割。
- 电力线检测交并比达95.53%，显著优于传统方法。
- 适合电力系统运维、智能巡检研发人员参考。
随着全球能源需求持续增长，保障电网安全与可靠运行至关重要。传统巡检方式如人工观测或直升机巡查成本高且难以扩展。本文探索利用3D计算机视觉自动化电网巡检，采用TS40K数据集——一个高密度、带标注的3D LiDAR点云集合。通过聚焦3D语义分割，解决类别不平衡与噪声数据问题，提升对电力线、铁塔等关键部件的检测能力。基准测试结果显示，基于变换器的模型在电力线检测上的交并比（IoU）达到95.53%，性能显著提升。研究证明了机器学习在电网维护流程中的应用潜力，可提高效率并支持主动风险管控策略。
原文摘要 · Abstract (English)
Ensuring the safety and reliability of power grids is critical as global energy demands continue to rise. Traditional inspection methods, such as manual observations or helicopter surveys, are resource-intensive and lack scalability. This paper explores the use of 3D computer vision to automate power grid inspections, utilizing the TS40K dataset -- a high-density, annotated collection of 3D LiDAR point clouds. By concentrating on 3D semantic segmentation, our approach addresses challenges like class imbalance and noisy data to enhance the detection of critical grid components such as power lines and towers. The benchmark results indicate significant performance improvements, with IoU scores reaching 95.53% for the detection of power lines using transformer-based models. Our findings illustrate the potential for integrating ML into grid maintenance workflows, increasing efficiency and enabling proactive risk management strategies.
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