摘要
高铁接触网是电气化铁路系统中的关键导线,保障其导线的正常状态对于维持铁路的稳定运营至关重要。传统的人工巡检方式效率低下且易漏检,随着深度学习技术的快速发展,利用计算机视觉技术实现自动化检测已成为迫切需求。针对高铁接触网室外多种复杂背景和多种环境(如夜晚、白天)下导线状态检测的挑战,文中提出了一种基于DEFM(细节增强融合模块)与YOLOv8结合的高铁接触网导线状态检测算法,通过结合空间和通道注意力机制将红外与可见光图像融合,引入多模态融合和Shuffle Attention注意力机制。通过在真实数据集上进行实验,验证了该模型在检测精度、召回率等性能指标上的显著提升。结果表明,改进后的算法相比原始算法,召回率提升了0.94%,mAP提升了2.09%。经实际测试,基于DEFM-YOLOv8的检测模型在面对高铁接触网复杂背景时,无论是在夜晚还是白天场景下,均能够取得良好的检测效果。
The high-speed rail contact network is a critical conductor in the electrified railway system,and ensuring the proper functioning of its wires is crucial for maintaining the stable operation of the railway.Traditional manual inspection methods are inefficient and prone to oversight.With the rapid development of deep learning technologies,the use of computer vision techniques for automated detection has become an urgent necessity.In response to the challenges of detecting the state of wires in high-speed rail contact networks under various complex outdoor backgrounds and diverse environmental conditions(such as night and day),this paper proposes a wire state detection algorithm based on the combination of a Detail Enhancement Fusion Module(DEFM)and YOLOv8.By incorporating spatial and channel attention mechanisms,the algorithm fuses infrared and visible light images,introducing multimodal fusion and the Shuffle Attention mechanism.Experiments conducted on a real dataset demonstrate the mo-del’s significant improvement in performance metrics such as detection accuracy and recall rate.The results show that the improved algorithm increases the recall rate by 0.94%and mAP by 2.09%compared to the original algorithms.Practical tests indicate that the DEFM-YOLOv8-based detection model performs effectively in detecting wires in the high-speed rail contact network,regardless of whether the environment is nighttime or daytime,even under complex backgrounds.
作者
高玉立
王宝会
GAO Yuli;WANG Baohui(School of Software,Beihang University,Beijing 100191,China)
出处
《计算机科学》
北大核心
2025年第S2期295-303,共9页
Computer Science
