TY - GEN
T1 - AUTOMATIC RETRIEVAL of RAILWAY MASTS TILT ANGLE from MOBILE LASER SCANNING DATA
AU - Marwati, Atika
AU - Wang, Chi Kuei
N1 - Publisher Copyright:
© ACRS 2021.All right reserved.
PY - 2021
Y1 - 2021
N2 - Mast plays an essential role in supporting objects, i.e., catenary, droppers, etc., in the railway electrification system. The condition of the tilt railway mast can influence the entire railway transportation safety. Therefore, the tilt angle of the railway masts needs to be retrieved to prevent damage. This study proposed an automatic method to retrieve the tilt angle masts by using Mobile Laser Scanning (MLS) point clouds data. Forward, the automatic method was tested into two-track locations, which in New Taipei city and Yilan county, in the north of Taiwan, respectively, had been used in our automatic detection method. The data was acquired two times, on July and October 2019. There were two main steps to retrieve the tilt condition of the railway masts. Firstly, point clouds were clustered by using Euclidean Distance Clustering and detected the masts by setting parameters. Secondly, the tilt angle of the mast was obtained by Principal Component Analysis (PCA) and was calculated based on vector-based. The result showed that 90% and 89% masts were detected in New Taipei City, respectively in the first and second acquisitions. In Yilan county, 82% masts were successfully detected. The RMSE of the tilt angle estimation result was 1.2° and 1.6°, respectively in New Taipei City and Yilan county.
AB - Mast plays an essential role in supporting objects, i.e., catenary, droppers, etc., in the railway electrification system. The condition of the tilt railway mast can influence the entire railway transportation safety. Therefore, the tilt angle of the railway masts needs to be retrieved to prevent damage. This study proposed an automatic method to retrieve the tilt angle masts by using Mobile Laser Scanning (MLS) point clouds data. Forward, the automatic method was tested into two-track locations, which in New Taipei city and Yilan county, in the north of Taiwan, respectively, had been used in our automatic detection method. The data was acquired two times, on July and October 2019. There were two main steps to retrieve the tilt condition of the railway masts. Firstly, point clouds were clustered by using Euclidean Distance Clustering and detected the masts by setting parameters. Secondly, the tilt angle of the mast was obtained by Principal Component Analysis (PCA) and was calculated based on vector-based. The result showed that 90% and 89% masts were detected in New Taipei City, respectively in the first and second acquisitions. In Yilan county, 82% masts were successfully detected. The RMSE of the tilt angle estimation result was 1.2° and 1.6°, respectively in New Taipei City and Yilan county.
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M3 - Conference contribution
AN - SCOPUS:85127400701
T3 - 42nd Asian Conference on Remote Sensing, ACRS 2021
BT - 42nd Asian Conference on Remote Sensing, ACRS 2021
PB - Asian Association on Remote Sensing
T2 - 42nd Asian Conference on Remote Sensing, ACRS 2021
Y2 - 22 November 2021 through 26 November 2021
ER -