目前铁路工务安全事故时有发生，现有的铁路工务安全监督方案效率低下且难以有效保障工务活动的安全进行。提出了一种基于改进YOLOv5的铁路工务人员防护装备检测方案，实现对铁路工务人员穿戴的安全帽和反光背心的实时检测。针对因铁路场景背景而造成的漏报、误报及检测不准确的问题，引入空间特征重构模块（spatial reconstruction unit, SRU）及通道注意力模块（efficient channel attention, ECA）在空间和通道两个维度上增强网络的特征表达能力，有效筛选有效特征并去除冗余特征；通过引入边界框损失W-IOU（weighted intersection over union）函数降低低质量样本对训练的影响，提升模型收敛能力。通过实验验证，模型平均准确率为94.3%，精确率为94.9%，召回率为94.7%，相比于基础网络分别提升了2.2%、2.5%和1.6%，推理速度达到34.5 FPS（帧／s），可以实现在真实工务环境中的实时检测。

Currently, railway maintenance safety accidents occur frequently, and existing railway safety supervision schemes are inefficient and difficult to effectively ensure the safe conduct of maintenance activities. A protection equipment detection scheme for railway maintenance workers based on the improved YOLOv5 model was proposed, which realized real-time detection of safety helmets and reflective vests worn by railway maintenance personnel. To address issues such as missed detections, false positives, and inaccurate detections caused by the complex background of railway scenes, a spatial reconstruction unit (SRU) and an efficient channel attention(ECA) module was introduced. These modules were employed to enhance the network's feature expression ability in both the spatial and channel dimensions, effectively filtering out useful features and removing redundant ones. Additionally, weighted intersection over union (W-IOU) loss function was introduced to reduce the impact of low-quality samples on training, thereby improving the convergence ability of model. Experimental validation result shows that the model achieves the average accuracy of 94.3%, the precision of 94.9%, and the recall of 94.7%, which represent the improvements of 2.2%, 2.5%, and 1.6%, respectively, over the baseline network. The inference speed reaches 34.5 FPS, allowing for real-time detection in actual maintenance environments.