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：在高风险铁路施工中，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。我们提出了一种创新模型YOLO-EA，该模型通过将高效通道注意力模块集成到其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。YOLO-EA进一步通过将广义交并比损失替换为高效交并比损失，改进了遮挡条件下的目标识别能力。YOLO-EA的有效性通过使用源自真实铁路施工现场监控视频的数据集进行了实证验证。其性能优于YOLOv5，实现了98.9%的精确率和94.7%的召回率，分别提升了2.5%和0.5%，同时保持了70.774帧/秒的实时性能。这种高效且精确的YOLO-EA模型在复杂施工场景的实际应用中具有巨大潜力，可在复杂的铁路施工项目中强制执行严格的安全合规要求。