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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Pantograph Detection Algorithm with Complex Background and External Disturbances.

Ping Tan¹, Zhisheng Cui¹, Wenjian Lv¹

¹School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.

Sensors (Basel, Switzerland)

|November 11, 2022

Summary

This summary is machine-generated.

This study introduces an improved pantograph detection algorithm for high-speed rail (HSR) using YOLO V4. The method enhances detection accuracy by addressing environmental interference, dirt, and complex backgrounds, ensuring safer HSR operations.

Keywords:

EOR-Brenner blob detection blur and dirt complex background high-speed railway object detection

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Area of Science:

Railway Engineering
Computer Vision
Artificial Intelligence

Background:

Pantograph status is critical for high-speed railway (HSR) operational safety.
Current pantograph detection methods struggle with environmental factors, interference, and low accuracy.

Purpose of the Study:

To develop a robust pantograph detection algorithm for HSR.
To overcome limitations of existing methods in real-world operating conditions.

Main Methods:

Real-time pantograph detection and localization using YOLO V4.
Blur and dirt detection for high-speed cameras (HSCs) using blob detection and improved Brenner method.
Complex background detection using grayscale and vertical projection.

Main Results:

Achieved high precision rates of 99.92%, 99.90%, and 99.98% on diverse test samples.
Demonstrated strong environmental adaptability in pantograph detection.
Effectively mitigated interference from complex backgrounds and external factors.

Conclusions:

The proposed algorithm significantly enhances pantograph detection reliability for HSR.
The method offers high practical application value for ensuring safe and efficient railway operations.