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A 3D LIDAR RECONSTRUCTION APPROACH FOR VEGETATION DETECTION IN POWER TRANSMISSION NETWORKS

التفاصيل البيبلوغرافية
العنوان: A 3D LIDAR RECONSTRUCTION APPROACH FOR VEGETATION DETECTION IN POWER TRANSMISSION NETWORKS
المؤلفون: Y. Ma, F. Zhou, G. Wen, H. Gen, R. Huang, Q. Wu, L. Pei
المصدر: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVI-3-W1-2022, Pp 141-148 (2022)
بيانات النشر: Copernicus Publications, 2022.
سنة النشر: 2022
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Applied optics. Photonics
مصطلحات موضوعية: Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
الوصف: Vegetation management is important to the power transmission and distribution networks. The encompassed towering tree is always the key factor of the high impedance faults(HIFs).LiDAR is an efficient way to detect trees with 3D point cloud. The classical tree detection algorithm can handle the tree with high and distinct trunk,but limited to the tree with messy trunks. While the deeplearning based detection algorithms are also suffered from the terrain noise points. In this paper, we propose an efficient LiDAR reconstruction system which can efficiently reconstruct the point cloud of surrounding vegetation without the ground plane noise. We also use different weight strategies to improve the localization accuracy. We have conducted our system on the real power network environment and the height detection result shows that our algorithm has a better accuracy and robustness compared with the classical methods.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1682-1750
2194-9034
Relation: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVI-3-W1-2022/141/2022/isprs-archives-XLVI-3-W1-2022-141-2022.pdf; https://doaj.org/toc/1682-1750; https://doaj.org/toc/2194-9034
DOI: 10.5194/isprs-archives-XLVI-3-W1-2022-141-2022
URL الوصول: https://doaj.org/article/d3d46aa69a65405ebd8a38bded7923c6
رقم الأكسشن: edsdoj.3d46aa69a65405ebd8a38bded7923c6
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:16821750
21949034
DOI:10.5194/isprs-archives-XLVI-3-W1-2022-141-2022