Jinhu Wang

Recent publications

1. 

   A Deep Learning Based Method for Railway Overhead Wire Reconstruction from Airborne LiDAR Data

   Lele Zhang, Jinhu Wang, Yueqian Shen, Jian Liang, Yuyu Chen, and 2 more authors

   Remote Sensing 2022

   Abs Bib HTML PDF Code

   Automatically and accurately reconstructing the overhead wires of railway from airborne laser scanning (ALS) data are an efficient way of railway monitoring to ensure stable and safety transportation services. However, due to the complex structure of the overhead wires, it is challenging to extract these wires using the existing methods. This work proposes a workflow for railway overhead wire reconstruction using deep learning for wire identification collaborating with the RANdom SAmple Consensus (RANSAC) algorithm for wire reconstruction. First, data augmentation and ground points down-sampling are performed to facilitate the issues caused by insufficient and non-uniformity of LiDAR points. Then, a network incorporating with PointNet model is proposed to segment wires, pylons and ground points. The proposed network is composed of a Geometry Feature Extraction (GFE) module and a Neighborhood Information Aggregation (NIA) module. These two modules are introduced to encode and describe the local geometric features. Therefore, the capability of the model to discriminate geometric details is enhanced. Finally, a wire individualization and multi-wire fitting algorithm is proposed to reconstruct the overhead wires. A number of experiments are conducted using ALS point cloud data of railway scenarios. The results show that the accuracy and MIoU for wire identification are 96.89% and 82.56%, respectively, which demonstrates a better performance compared to the existing methods. The overall reconstruction accuracy is 96% over the study area. Furthermore, the presented strategy also demonstrated its applicability to high-voltage powerline scenarios.

   @article{Zhang2022,
     author = {Zhang, Lele and Wang, Jinhu and Shen, Yueqian and Liang, Jian and Chen, Yuyu and Chen, Linsheng and Zhou, Mei},
     title = {A Deep Learning Based Method for Railway Overhead Wire Reconstruction from Airborne LiDAR Data},
     journal = {Remote Sensing},
     volume = {14},
     year = {2022},
     number = {20},
     article-number = {5272},
     url = {https://www.mdpi.com/2072-4292/14/20/5272},
     issn = {2072-4292},
     doi = {10.3390/rs14205272},
   }

2. 

   Plot-level reconstruction of 3D tree models for aboveground biomass estimation

   Guangpeng Fan, Zhenyu Xu, Jinhu Wang, Liangliang Nan, Huijie Xiao, and 2 more authors

   Ecological Indicators 2022

   Abs Bib HTML PDF

   Complexity of forest structure is an important factor contributing to uncertainty in aboveground biomass estimates. In this study, we present a new method for reducing uncertainty in forest aboveground biomass (AGB) estimation based on plot-level terrestrial laser scanner (TLS) point clouds reconstruction. The method estimates the total AGB of plots with complex structures after automatically performing the steps of ground point filtering, single tree segmentation, and three-dimensional (3D) structure reconstruction. We used plot data from temperate and tropical forest ecosystems to verify the effectiveness of the method, reconstructing a 1300 m2 temperate plantation plot and a 5000 m2 mingled forest plot, respectively. The total biomass of 153 trees in the plantation plot was overestimated by 17.12 %, and the total biomass of 61 trees in the mingled forest plot was underestimated by 10.88 %. We found that the uncertainty of aboveground biomass estimation in tropical forests with more complex structures is not necessarily greater than in plantations. Therefore, in large-scale remote sensing observations of forest biomass, the number or area of plots can be increased to reduce the uncertainty of the results caused by the complex structure. The focus of this study is to explore TLS point clouds modeling methods to reduce the uncertainty in AGB estimation caused by the complexity of forest structures, and to provide reference cases for plot-level point clouds reconstruction methods. Forest ecologists can use this method to regularly observe forest growth and obtain indicators related to forest ecology without destroying trees.

   @article{Fan2022,
     title = {Plot-level reconstruction of 3D tree models for aboveground biomass estimation},
     journal = {Ecological Indicators},
     volume = {142},
     pages = {109211},
     year = {2022},
     issn = {1470-160X},
     doi = {https://doi.org/10.1016/j.ecolind.2022.109211},
     url = {https://www.sciencedirect.com/science/article/pii/S1470160X22006835},
     author = {Fan, Guangpeng and Xu, Zhenyu and Wang, Jinhu and Nan, Liangliang and Xiao, Huijie and Xin, Zhiming and Chen, Feixiang},
     keywords = {Complex forest structure, Above-ground biomass, Estimation uncertainty, Plot-level, TLS point clouds},
   }

3. 

   Road marking detection and extraction method based on neighborhood density and Kalman Filter

   Xiaoyu Li, Mei Zhou, Jinhu Wang, and Qiangqiang Yao

   Journal of University of Chinese Academy of Sciences 2022

   Abs Bib HTML

   This paper presents a methodology for detection and extraction of road marking based on neighborhood point density and Kalman Filter from mobile laser scanning in three steps: 1) Segmenting road point clouds into scan lines; 2) Generating convolution kernel based on point density in neighborhood for extracting road marking contour points; 3)Fitting contour line using the least squares algorithm and completing omitted road markings using Kalman Filter. A quantitative validation shows that the average deviation of center points and orientation are 0.04 m and 0.04 respectively, and the average completeness of results are 99.69%. The proposed method reduces the influence of unevenly distributed points on road markings extraction and improves the overall extraction completeness.

   @article{Li2022,
     author = {Li, Xiaoyu and Zhou, Mei and Wang, Jinhu and Yao, Qiangqiang},
     journal = {Journal of University of Chinese Academy of Sciences},
     number = {2},
     title = {Road marking detection and extraction method based on neighborhood density and Kalman Filter},
     volume = {4},
     year = {2022},
     doi = {10.7523/j.ucas.2022.024},
   }