Research on Railway Obstacle Detection Method Based on Developed Euclidean Clustering
To prevent the problem of safety accidents caused by the intrusion of obstacles into railway clearance, this paper proposes an obstacle detection method based on Light Detection and Ranging (LiDAR) to obtain and process rich three-dimensional (3D) information and depth information of the railway sce...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/5/1175 |
| _version_ | 1797615509015887872 |
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| author | Jinyan Qu Shaobin Li Yanman Li Liu Liu |
| author_facet | Jinyan Qu Shaobin Li Yanman Li Liu Liu |
| author_sort | Jinyan Qu |
| collection | DOAJ |
| description | To prevent the problem of safety accidents caused by the intrusion of obstacles into railway clearance, this paper proposes an obstacle detection method based on Light Detection and Ranging (LiDAR) to obtain and process rich three-dimensional (3D) information and depth information of the railway scene. The method first preprocesses the point cloud of the railway scenario collected by LiDAR to divide a basic area containing the rails. Then, the method divides the roadbed plane and fits the rails with the random sample consensus (RANSAC) algorithm, dividing the detection area according to the position of the rails. To address the issue of over or under-segmentation in the traditional Euclidean clustering method, which is due to sparser point clouds the farther the object is from the LiDAR, this paper improves the conventional Euclidean clustering. It introduces an adaptive distance threshold to categorize obstacles. Finally, compared with traditional Euclidean clustering, K-means clustering, and density-based spatial clustering of applications with noise (DBSCAN) clustering, the improved Euclidean cluster has achieved better results in terms of computing time and segmentation accuracy. Experimental results show the ability of the method to detect railway obstacles successfully. |
| first_indexed | 2024-03-11T07:27:31Z |
| format | Article |
| id | doaj.art-47a0f04f332c4f25934180c72110dd67 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T07:27:31Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-47a0f04f332c4f25934180c72110dd672023-11-17T07:32:43ZengMDPI AGElectronics2079-92922023-02-01125117510.3390/electronics12051175Research on Railway Obstacle Detection Method Based on Developed Euclidean ClusteringJinyan Qu0Shaobin Li1Yanman Li2Liu Liu3School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, ChinaTo prevent the problem of safety accidents caused by the intrusion of obstacles into railway clearance, this paper proposes an obstacle detection method based on Light Detection and Ranging (LiDAR) to obtain and process rich three-dimensional (3D) information and depth information of the railway scene. The method first preprocesses the point cloud of the railway scenario collected by LiDAR to divide a basic area containing the rails. Then, the method divides the roadbed plane and fits the rails with the random sample consensus (RANSAC) algorithm, dividing the detection area according to the position of the rails. To address the issue of over or under-segmentation in the traditional Euclidean clustering method, which is due to sparser point clouds the farther the object is from the LiDAR, this paper improves the conventional Euclidean clustering. It introduces an adaptive distance threshold to categorize obstacles. Finally, compared with traditional Euclidean clustering, K-means clustering, and density-based spatial clustering of applications with noise (DBSCAN) clustering, the improved Euclidean cluster has achieved better results in terms of computing time and segmentation accuracy. Experimental results show the ability of the method to detect railway obstacles successfully.https://www.mdpi.com/2079-9292/12/5/1175LiDARpoint cloudobstacle detectionRANSACimproved Euclidean clustering |
| spellingShingle | Jinyan Qu Shaobin Li Yanman Li Liu Liu Research on Railway Obstacle Detection Method Based on Developed Euclidean Clustering Electronics LiDAR point cloud obstacle detection RANSAC improved Euclidean clustering |
| title | Research on Railway Obstacle Detection Method Based on Developed Euclidean Clustering |
| title_full | Research on Railway Obstacle Detection Method Based on Developed Euclidean Clustering |
| title_fullStr | Research on Railway Obstacle Detection Method Based on Developed Euclidean Clustering |
| title_full_unstemmed | Research on Railway Obstacle Detection Method Based on Developed Euclidean Clustering |
| title_short | Research on Railway Obstacle Detection Method Based on Developed Euclidean Clustering |
| title_sort | research on railway obstacle detection method based on developed euclidean clustering |
| topic | LiDAR point cloud obstacle detection RANSAC improved Euclidean clustering |
| url | https://www.mdpi.com/2079-9292/12/5/1175 |
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