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An Intelligent Obstacle Detection Method for Rail Transit Scenarios.

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  • 1Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China.

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Summary

This study introduces ACX-YOLOv8, an intelligent obstacle detection system for railway tracks. The enhanced YOLOv8 model accurately identifies foreign objects in real-time, improving train operational safety.

Keywords:
CDConvYOLOv8detection decoupled headrailway obstacle detectionvisual sensing

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Railway Engineering

Background:

  • Traditional railway monitoring systems lack real-time foreign object detection capabilities.
  • Ensuring train operational safety requires advanced obstacle identification methods.

Purpose of the Study:

  • To develop an intelligent visual sensing approach for detecting foreign objects in railway track regions.
  • To enhance the accuracy and efficiency of obstacle detection using an improved YOLOv8 model.

Main Methods:

  • The ACX-YOLOv8 model integrates spatial coordinate attention mechanism (SCAM) and cascaded dilated convolution (CDConv).
  • An X6 decoupled detection head is employed to improve detection accuracy and inference speed.
  • The model is built upon the YOLOv8 framework for object detection.

Main Results:

  • The ACX-YOLOv8 model achieved a mean average precision (mAP50) of 87.1%, a 2.7% improvement over the baseline YOLOv8.
  • The model demonstrated a 1.8% mAP50 increase on the PASCAL VOC dataset, verifying its generalization ability.
  • The optimized model has 4.85 million parameters, balancing lightweight properties with high detection precision.

Conclusions:

  • ACX-YOLOv8 provides rapid and accurate detection of foreign objects in railway tracks.
  • The algorithm offers reliable technical support for real-time obstacle monitoring in complex railway environments.
  • This contributes to enhancing the safety and intelligentization of railway operations.