UAV-LiDAR-Based Measuring Framework for Height and Stagger of High-Speed Railway Contact Wire

Geng, Yixuan; Pan, Fengjun; Jia, Limin; Wang, Zhipeng; Qin, Yong; Tong, Lei; Li, Shiqi

doi:10.1109/tits.2021.3071445

Cited by 24 publications

(8 citation statements)

References 25 publications

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“…Railway contact wire monitoring: The condition of contact wires is critically important to the safety of high-speed railways. Geng et al (2021) demonstrated that drone-based LiDAR sensing can be effective in measuring the spatial distribution of geometric characteristics such as contact wire height and stagger (Geng, et al, 2021).…”

Section: Railway Tunnel Monitoringmentioning

confidence: 99%

Systematic Literature Review of Drone Utility in Railway Condition Monitoring

Askarzadeh

Bridgelall

Tolliver

2023

J. Transp. Eng., Part A: Systems

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Drones have recently become a new tool in railway inspection and monitoring (RIM) worldwide, but there is still a lack of information about the specific benefits and costs. This study conducts a systematic literature review (SLR) of the applications, opportunities, and challenges of using drones for RIM. The SLR technique yielded 47 articles filtered from 7,900 publications from 2014 to 2022. The SLR found that key motivations for using drones in RIM are to reduce costs, improve safety, save time, improve mobility, increase flexibility, and enhance reliability. Nearly all the applications fit into the categories of defect identification, situation assessment, rail network mapping, infrastructure asset monitoring, track condition monitoring, and obstruction detection. The authors assessed the open technical, safety, and regulatory challenges. The authors also contributed a cost analysis framework, identified factors that affect drone performance in RIM, and offered implications for new theories, management, and impacts to society.

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Section: Railway Tunnel Monitoringmentioning

confidence: 99%

Systematic Literature Review of Drone Utility in Railway Condition Monitoring

Askarzadeh

Bridgelall

Tolliver

2023

J. Transp. Eng., Part A: Systems

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“…In order to improve the generalizability of the network in diferent scenes, it will be necessary for future applications to collect point cloud data from a variety of scenes as a training set for the network. In addition, the incorporation of multisource data, such as images and LiDAR [41], can be considered to improve segmentation precision.…”

Section: Generalizabilitymentioning

confidence: 99%

Fine-Grained Point Cloud Semantic Segmentation of Complex Railway Bridge Scenes from UAVs Using Improved DGCNN

Qiu,

Liu,

Peng

et al. 2023

Structural Control and Health Monitoring

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Automatic semantic segmentation of point clouds in railway bridge scenes is a crucial step in the digitization process and is required for a variety of subapplications including digital twin reconstruction and component geometric quality verification. This paper details a method for reliably and effectively segmenting point clouds acquired from complex railway bridge scenes by unmanned aerial vehicles (UAVs). The method involves segmenting seven common infrastructure elements in railway bridge point clouds using an improved DGCNN after processing low-quality point clouds from UAVs with a score-based denoising algorithm. The segmentation performance of the network is measured by averaging the intersection to union ratio between the segmentation results and the true labels of different elements, i.e., the mean intersection over union (mIoU). The proposed method is evaluated on three different scenes of railway bridges and achieved mIoU values of 99.18%, 90.76%, and 85.84%, respectively, at three levels of complexity ranging from easy to difficult. The results demonstrate that the proposed method captures the most discriminative features from low-quality point clouds, allowing for the accurate and efficient digital representation of railway bridge scenes.

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“…Scanning drones are also used in railway infrastructure surveys. Publication [45] presents a self-adapting algorithm for extraction of rails, wires and masts based on their specific geometric features in the UAV lidar cloud. Some research works are devoted to the use of UAV lidar for monitoring high-voltage lines [46,47].…”

Section: Literature Reviewmentioning

confidence: 99%

Study of the Precise Determination of Pipeline Geometries Using UAV Scanning Compared to Terrestrial Scanning, Aerial Scanning and UAV Photogrammetry

Lenda,

Borowiec,

Marmol

2023

Sensors

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Transmission pipelines belong to technical infrastructure, the condition of which is subject to periodic monitoring. The research was to verify whether aerial measurement methods, especially UAV laser scanning, could determine the geometric shape of pipelines with a precision similar to that of terrestrial scanning, adopted as a reference method. The test object was a section of a district heating pipeline with two types of surfaces: matte and glossy. The pipeline was measured using four methods: terrestrial scanning, airborne scanning, UAV scanning and the structure from motion method. Then, based on the reference terrestrial scanning data, pipeline models were created, with which all methods were compared. The comparison made it possible to find that only the UAV scanning yielded results consistent with those of the terrestrial scanning for all the pipes. The differences usually did not exceed 10 mm, sometimes reaching 20 mm. The structure from motion method yielded unstable results. For the old, matte pipes, the results were similar to those of the UAV scan; however, for the new, shiny pipes, the differences were up to 60 mm.

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UAV-LiDAR-Based Measuring Framework for Height and Stagger of High-Speed Railway Contact Wire

Cited by 24 publications

References 25 publications

Systematic Literature Review of Drone Utility in Railway Condition Monitoring

Systematic Literature Review of Drone Utility in Railway Condition Monitoring

Fine-Grained Point Cloud Semantic Segmentation of Complex Railway Bridge Scenes from UAVs Using Improved DGCNN

Study of the Precise Determination of Pipeline Geometries Using UAV Scanning Compared to Terrestrial Scanning, Aerial Scanning and UAV Photogrammetry

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