Railway freight loading state image recognition method and system

By introducing bidirectional weighted BiFPN and an improved coordinate attention mechanism into railway freight inspection, the problem of low detection accuracy of abnormal freight car loading status has been solved, intelligent identification and alarm have been realized, and the quality and detection accuracy of freight inspection work have been improved.

CN117011798BActive Publication Date: 2026-07-24CHINA RAILWAY FIRST SURVEY & DESIGN INST GRP
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Patent Information

Application Number
CN202311034149.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-08-16
Publication Date
2026-07-24
Estimated Expiration
2043-08-16

AI Technical Summary

Technical Problem

The problem with existing railway freight inspection technology is its low precision, especially in detecting abnormal loading conditions of freight cars. This leads to a reliance on manual inspection for quality control, which can easily result in missed or incorrect inspections.

Method used

A railway freight loading status image recognition method is adopted. By establishing an anomaly dataset and enhancing it, a bidirectional weighted BiFPN structure and an embedded coordinate attention mechanism module are introduced into the YOLOv5 network model. Feature fusion is performed using feature pyramids and an improved coordinate attention mechanism to improve detection accuracy.

Benefits of technology

It significantly improves the detection accuracy of railway freight loading status identification, reduces the labor intensity of operators, improves the quality of freight inspection work, and realizes intelligent identification and alarm of freight car loading status.

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Abstract

The present application relates to a kind of railway freight loading state image recognition method and system.Current freight car loading state anomaly detection method has the problem of low detection accuracy.This method is based on railway freight vehicle abnormal image to establish abnormal data set, and is enhanced and expanded;Introduce bidirectional weighted BiFPN structure in YOLOv5 network model;Coordinate attention mechanism module is embedded in bidirectional weighted BiFPN structure;Abnormal data set is input into model for training;Target detection data set is input into the model after training for railway freight vehicle anomaly recognition.The present application uses weighted bidirectional feature pyramid to capture information on context, improves the fusion degree of different weight multiscale features, uses embedded improved coordinate attention module, makes receptive field more focused on interesting area, maintains the integrity of features, significantly improves detection accuracy, achieves the purpose of reducing operator labor intensity and improving freight inspection work quality.
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