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Related Concept Videos

Leveling Equipment01:18

Leveling Equipment

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As leveling involves measuring vertical distances relative to a horizontal line of sight, it requires a graduated rod, called a level rod, for vertical measurements and an instrument called a level for a horizontal sight line. A level includes a high-powered telescope with a mechanism for leveling to ensure the line of sight is horizontal when the bubble in the spirit level is centered. Leveling rods, made of wood, metal, or fiberglass, are graduated in feet or meters and commonly used in two-...
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Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over...
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Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...
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Edge Intelligence-Based Rail Transit Equipment Inspection System.

Lijia Tian1, Hongli Zhao1, Li Zhu1

  • 1State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing 100044, China.

Sensors (Basel, Switzerland)
|January 10, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an Edge Intelligence (EI) and 5G system for automated rail transit equipment inspection, significantly improving safety and efficiency. The system reduces detection latency by 45% and bandwidth use by 98.1%.

Keywords:
5GYOLOv8computer visionedge intelligenceintelligent inspectionobject detectionrail transit

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Area of Science:

  • Intelligent transportation systems
  • Edge computing in critical infrastructure
  • Computer vision for industrial automation

Background:

  • Rail transit safety depends on reliable equipment maintenance, but manual inspections are inefficient and error-prone.
  • Traditional methods struggle with the high costs, low efficiency, and human error associated with manual rail equipment checks.
  • Automating inspection processes is crucial for enhancing the safety and operational efficiency of rail transit.

Purpose of the Study:

  • To develop and evaluate an automated rail transit equipment inspection system using Edge Intelligence (EI) and 5G technology.
  • To enhance the safety, real-time performance, security, and robustness of rail transit maintenance.
  • To address the limitations of traditional manual inspection methods in rail transport.

Main Methods:

  • A cloud-edge-end collaborative architecture integrating Computer Vision (CV) for automated inspection.
  • Utilized a fine-tuned YOLOv8 model for object detection and a ResNet-18 network for equipment status classification.
  • Implemented an ETSI MEC-compliant framework on edge servers (NVIDIA Jetson AGX Orin) within a 5G private network.

Main Results:

  • YOLOv8 achieved 92.7% mAP@0.5 for equipment detection; ResNet-18 attained 95.8% accuracy for status classification.
  • Reduced average end-to-end latency for anomaly detection by 45% (28.5 ms vs. 52.1 ms) compared to cloud-centric systems.
  • Lowered daily bandwidth consumption by approximately 98.1% (40.0 GB to 0.76 GB) via event-triggered evidence uploads.

Conclusions:

  • The EI and 5G-based system offers superior real-time performance, enhanced security, and improved robustness for rail transit equipment inspection.
  • The automated system significantly outperforms cloud-centric approaches in terms of latency and bandwidth efficiency.
  • The deployed system on Beijing Metro Line 6 demonstrates practical viability and effectiveness in real-world scenarios.