A force partition weighted railway bed dirt visual detection method and system

By combining adaptive sampling and hierarchical visual recognition with a mechanical weighted model, the problem of low efficiency and high misjudgment rate in track bed dirt detection in existing technologies has been solved, achieving efficient and accurate track bed dirt detection and risk assessment, and supporting intelligent maintenance decisions.

CN122415962APending Publication Date: 2026-07-17CHONGQING UNIV OF TECH
View PDF 0 Cites 0 Cited by

Patent Information

Application Number
CN202610403109.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-30
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing railway track bed contamination detection technologies suffer from low efficiency, high error rate, high computational resource consumption, and lack of mechanical integration, making it difficult to achieve efficient and accurate detection and risk assessment.

Method used

By constructing an adaptive sampling mechanism based on train motion parameters, valid image frames are screened, and a hierarchical visual recognition model and a mechanical weighted model are adopted to generate a comprehensive risk index, thereby achieving accurate detection and risk assessment of track bed contamination.

Benefits of technology

Significantly reduce computing resource consumption, improve detection efficiency, enhance detection accuracy, form a closed loop of detection-evaluation-decision, and achieve accurate track bed contamination risk assessment and maintenance decisions.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122415962A_ABST
    Figure CN122415962A_ABST
Patent Text Reader

Abstract

The application discloses a kind of force partition weighted railway bed dirt visual detection method and system, belong to railway engineering intelligent detection technical field.The application constructs the dynamic mapping model of interframe displacement and field of view overlap rate by real-time acquisition train motion parameter, and effectively frame is adaptively filtered;Adopt coarse cutting-fine identification hierarchical parallel vision architecture to identify dirty area;Based on the stress characteristics of bed, high stress area and low stress area are divided, and a mechanical weighting model is introduced to calculate the comprehensive risk index;The risk index is associated with the geographic location to generate a risk space distribution map, triggering hierarchical early warning and maintenance decision.The application solves the problems of data redundancy, heavy computing burden, and the disconnection between evaluation indicators and stress state in the prior art, achieving efficient and accurate detection of bed dirt and risk quantification, providing a scientific basis for railway maintenance decisions, and being suitable for heavy haul railways, coal lines and other scenarios.
Need to check novelty before this filing date? Find Prior Art