铁道科学与工程学报2025，Vol.22Issue(3)：1328-1343,16.DOI:10.19713/j.cnki.43-1423/u.T20241849
基于机器视觉的铁路限界入侵检测方法
Railway boundary foreign object intrusion detection method based on machine vision
摘要
Abstract
When foreign objects invade railway boundaries,they pose a great threat to railway operation safety and passenger life and property safety.Common invasion objects include idle personnel,falling rocks,tree branches,etc.However,it is difficult to identify small-scale and small sample invasion objects in complex open railway environments.A railway boundary foreign object intrusion detection method based on feature focused diffusion network was proposed to address the above issues.Firstly,in response to the computing power constraints of edge computing devices,the backbone network structure of the benchmark model had been improved to reduce computational consumption while maintaining similar detection accuracy.Secondly,a feature focused diffusion pyramid network was proposed to improve the neck network structure of the benchmark model,enhance the effective interaction between features at different levels,and improve the ability to recognize objects at different scales.Then,by using dynamic detection heads to improve the benchmark model,the situation of losing fine-grained feature information of objects in deep networks was improved.Finally,by improving the loss function,the model pays more attention to the object feature information of small samples and difficult to recognize types,and effectively enhances its recognition ability in such situations.In response to the problem of limited real samples of foreign object intrusion in railways,a dataset was constructed by simulating and collecting a large amount of foreign object intrusion data from different scenarios.The experimental results show that by adding improvement modules,the recognition accuracy of the method proposed in this paper continues to improve,and the average accuracy of the improved model reaches 94.9%,which is 3.7 percentage points higher than that of the baseline model.Compared with various mainstream object detection methods,the improvement in small object recognition ability is the most significant,with a recognition rate of the highest 91.3%.The research results can indicate that the improved model in this paper can effectively identify intrusion objects in complex railway environments and has good application value.关键词
铁路运输/限界入侵/特征聚焦扩散金字塔网络/动态检测头/损失函数Key words
railway transportation/foreign object intrusion/feature focused diffusion pyramid network/dynamic head/loss function分类
信息技术与安全科学引用本文复制引用
杨文,胡昊,李凌志,冯爽,吴浩楠..基于机器视觉的铁路限界入侵检测方法[J].铁道科学与工程学报,2025,22(3):1328-1343,16.基金项目
中国国家铁路集团有限公司重大课题(K2023T003) （K2023T003）
国家重点研发计划项目(2022YFC3005202) （2022YFC3005202）
中国铁道科学研究院集团有限公司院基金课题(2023YJ088) （2023YJ088）
国家自然科学基金联合基金资助项目(U2268217) （U2268217）
