ReviewSensors (Basel, Switzerland)2024
A Survey on Multi-Sensor Fusion Perimeter Intrusion Detection in High-Speed Railways.
Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Wind energy-driven rabbit hair TENG for railway detection.iScience · 2026Article
- From Point Clouds to Predictive Maintenance: A Review of Intelligent Railway Infrastructure Monitoring.Sensors (Basel, Switzerland) · 2026Review
- Sensor-Based Cyber Risk Management in Railway Infrastructure Under the NIS2 Directive.Sensors (Basel, Switzerland) · 2025Article
- Robust Pedestrian Detection and Intrusion Judgment in Coal Yard Hazard Areas via 3D LiDAR-Based Deep Learning.Sensors (Basel, Switzerland) · 2025Article
- Railway Intrusion Risk Quantification with Track Semantic Segmentation and Spatiotemporal Features.Sensors (Basel, Switzerland) · 2025Article
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
In recent years, the safety issues of high-speed railways have remained severe. The intrusion of personnel or obstacles into the perimeter has often occurred in the past, causing derailment or parking, especially in the case of bad weather such as fog, haze, rain, etc. According to previous research, it is difficult for a single sensor to meet the application needs of all scenario, all weather, and all time domains. Due to the complementary advantages of multi-sensor data such as images and point clouds, multi-sensor fusion detection technology for high-speed railway perimeter intrusion is becoming a research hotspot. To the best of our knowledge, there has been no review of research on multi-sensor fusion detection technology for high-speed railway perimeter intrusion. To make up for this deficiency and stimulate future research, this article first analyzes the situation of high-speed railway technical defense measures and summarizes the research status of single sensor detection. Secondly, based on the analysis of typical intrusion scenarios in high-speed railways, we introduce the research status of multi-sensor data fusion detection algorithms and data. Then, we discuss risk assessment of railway safety. Finally, the trends and challenges of multi-sensor fusion detection algorithms in the railway field are discussed. This provides effective theoretical support and technical guidance for high-speed rail perimeter intrusion monitoring.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.