In high-risk railway construction, personal protective equipment monitoring is critical but challenging due to small and frequently obstructed targets. We propose YOLO-EA, an innovative model that enhances safety measure detection by integrating ECA into its backbone's convolutional layers, improving discernment of minuscule objects like hardhats. YOLO-EA further refines target recognition under occlusion by replacing GIoU with EIoU loss. YOLO-EA's effectiveness was empirically substantiated using a dataset derived from real-world railway construction site surveillance footage. It outperforms YOLOv5, achieving 98.9% precision and 94.7% recall, up 2.5% and 0.5% respectively, while maintaining real-time performance at 70.774 fps. This highly efficient and precise YOLO-EA holds great promise for practical application in intricate construction scenarios, enforcing stringent safety compliance during complex railway construction projects.

翻译：在铁路高危施工环境中，个人防护装备监测至关重要，但由于目标尺寸小且频繁被遮挡，该任务极具挑战性。本文提出YOLO-EA创新模型，通过将高效通道注意力模块（ECA）集成至主干网络的卷积层中，增强了对安全帽等微小物体的辨别能力，从而提升了安全措施检测性能。为进一步优化遮挡条件下的目标识别，模型采用EIoU损失函数替代原GIoU损失。基于真实铁路施工现场监控视频构建的数据集进行了实证验证，结果表明YOLO-EA在保持70.774帧/秒实时处理速度的同时，其精确率与召回率分别达到98.9%和94.7%，较YOLOv5提升2.5%和0.5%。这种高效精准的YOLO-EA模型在复杂施工场景中具有广阔的应用前景，可为铁路复杂工程项目中的严格安全规范执行提供有力支持。