计算机工程与应用2025，Vol.61Issue(14)：343-352,10.DOI:10.3778/j.issn.1002-8331.2503-0081
基于跨视图查询一致性的铁路轨道异物检测方法
Cross-View Query Consistency-Based Semi-Supervised Method for Railway Foreign Object Detection
摘要
Abstract
Railway track foreign object detection plays a crucial role in ensuring the safe operation of railways.However,this field currently faces two major challenges:data scarcity and high annotation costs.Some anomalies on the tracks are relatively rare,making it difficult for existing public datasets to cover a wide range of abnormal scenarios.Furthermore,manual data annotation is not only time-consuming and labor-intensive but also struggles to meet the demands of large-scale applications.To address these challenges,this paper proposes a novel framework for railway track foreign object image generation and detection,combining foreign object image generation with semi-supervised learning strategies to enhance detection performance.Specifically,the paper introduces a multi-region guided foreign object generation method based on a diffusion model,which can simultaneously generate realistic railway foreign object images across multiple regions while maintaining overall stylistic consistency.This approach effectively mitigates the problem of insufficient real data.Additionally,the paper develops a semi-supervised detection framework based on cross-view query consistency,which addresses the issue of noisy pseudo-labels in the teacher-student framework by learning more robust semantic fea-tures across different augmented views.Extensive experimental results demonstrate that the proposed method significantly improves both the accuracy and robustness of track foreign object detection,providing an efficient and reliable solution for safe railway operations.关键词
轨道异物检测/图像生成/半监督学习Key words
track obstacle detection/image generation/semi-supervised learning分类
信息技术与安全科学引用本文复制引用
蒋伟力,王少奇,冀振燕..基于跨视图查询一致性的铁路轨道异物检测方法[J].计算机工程与应用,2025,61(14):343-352,10.基金项目
校优秀大创项目(20231000411432). （20231000411432）
