Advancing Railway Infrastructure Monitoring: A Case Study on Railway Pole Detection
Yuxin Yan, Huirui Wang, Jingyi Wen, Zerong Lan, Liang Wang*
School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China
* Corresponding Author: Liang Wang. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.057949
Received 31 August 2024; Accepted 27 November 2024; Published online 18 March 2025
Abstract
The development of artificial intelligence (AI) technologies creates a great chance for the iteration of railway monitoring. This paper proposes a comprehensive method for railway utility pole detection. The framework of this paper on railway systems consists of two parts: point cloud preprocessing and railway utility pole detection. This method overcomes the challenges of dynamic environment adaptability, reliance on lighting conditions, sensitivity to weather and environmental conditions, and visual occlusion issues present in 2D images and videos, which utilize mobile LiDAR (Laser Radar) acquisition devices to obtain point cloud data. Due to factors such as acquisition equipment and environmental conditions, there is a significant amount of noise interference in the point cloud data, affecting subsequent detection tasks. We designed a Dual-Region Adaptive Point Cloud Preprocessing method, which divides the railway point cloud data into track and non-track regions. The track region undergoes projection dimensionality reduction, with the projected results being unique and subsequently subjected to 2D density clustering, greatly reducing data computation volume. The non-track region undergoes PCA-based dimensionality reduction and clustering operations to achieve preprocessing of large-scale point cloud scenes. Finally, the preprocessed results are used for training, achieving higher accuracy in utility pole detection and data communication. Experimental results show that our proposed preprocessing method not only improves efficiency but also enhances detection accuracy.
Keywords
Railway pole detection; point cloud data; mobile LiDAR; dual-region adaptive method; PCA-based dimensionality reduction
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