Railway obstacle intrusion monitoring based on point cloud and image fusion

Xiaoguang Chen; Peichuan Wang; Suining Wu

doi:10.1117/12.3011595

7 December 2023 Railway obstacle intrusion monitoring based on point cloud and image fusion

Xiaoguang Chen, Peichuan Wang, Suining Wu

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129413T (2023) https://doi.org/10.1117/12.3011595
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

A fusion detection method using multi line lidar point cloud detection combined with image recognition analysis is proposed to address the hazards of obstacles such as railway landslides and falling rocks. After clustering based on Euclidean distance, it is proposed to apply real scene restriction information to filter out interference clustering, while combining inter frame information to greatly reduce the false alarm rate of point cloud detection. Image detection uses YOLO-V5 for object detection and proposes to use a whole target sequence to achieve target information matching. On the basis of unifying the coordinate system of radar and camera, a "Intersection small ratio" method is proposed to achieve the fusion of camera and radar detection results. The research results indicate that the obstacle detection method combining point cloud and image can accurately and reliably detect people, trains, and various types of obstacles within the region, providing new technical support for railway line state perception.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaoguang Chen, Peichuan Wang, and Suining Wu "Railway obstacle intrusion monitoring based on point cloud and image fusion", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129413T (7 December 2023); https://doi.org/10.1117/12.3011595

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KEYWORDS

Target detection

Image fusion

Point clouds

Radar sensor technology

Tunable filters

Cameras

LIDAR

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