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。我们提出YOLO-EA这一创新模型，通过将高效通道注意力机制集成至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函数替换为高效交并比损失函数，优化了遮挡条件下的目标识别能力。基于真实铁路施工现场监控视频构建的数据集进行的实证研究验证了YOLO-EA的有效性。该模型在保持70.774帧/秒实时处理速度的同时，性能优于YOLOv5，精确率达到98.9%，召回率达到94.7%，分别提升了2.5%和0.5%。这种高效且精确的YOLO-EA模型在复杂施工场景中具有广阔的实际应用前景，可在铁路复杂工程项目中严格执行安全合规要求。