基于注意力机制的铁路入侵目标识别方法OACSTPCD
Attention Mechanism-Based Method for Intrusion Target Recognition in Railway
异物入侵检测对于保障铁路运营安全十分重要,针对传统铁路综合视频监控效率低、检测精度差以及现有智能检测算法检测速度慢等问题,结合注意力机制和目标检测模型在边端进行入侵目标检测.在提高检测精度方面,将包括空间注意力模块和通道注意力模块的卷积注意力模块(Convolutional block attention module,CBAM)模块融合到YOLOv5模型当中,构建了CBAM-YOLOv5模型,并采用距离交并比非极大值抑制(Dis-tance i…查看全部>>
The detection of foreign object intrusion is crucial for ensuring the safety of railway operations.To address challenges such as low efficiency,suboptimal detection accuracy,and slow detection speed inherent in conventional comprehensive video monitoring systems for railways,a railway foreign object intrusion recognition and detection system is conceived and implemented using edge computing and deep learning technologies.In a bid to raise detection accuracy,…查看全部>>
石江;白丁元;郭保青;王尧;阮涛
国能朔黄铁路发展有限责任公司,北京 100080,中国北京交通大学机械与电子控制工程学院,北京 100044,中国北京交通大学机械与电子控制工程学院,北京 100044,中国北京交通大学机械与电子控制工程学院,北京 100044,中国北京交通大学机械与电子控制工程学院,北京 100044,中国
电子信息工程
异物检测铁路防护边缘计算空间注意力模块通道注意力模块
foreign object detectionrailway protectionedge computingspatial attention modulechannel attention module
《南京航空航天大学学报（英文版）》 2024 (4)
541-554,14
This work was supported in part by the Science and Technology Innovation Project of CHN En-ergy ShuoHuang Railway Development Company Ltd(No.SHTL-22-28),the Beijing Natural Science Foundation-Fengtai Urban Rail Transit Frontier Research Joint Fund(No.L231002),and the Major Project of China State Rail-way Group Co.,Ltd.(No.K2023T003).
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