基于改进YOLOv5的安全防护装备目标检测
Target Detection of Safety Protective Gear Using the Improved YOLOv5
AI总结：
针对铁路施工中安全防护装备检测难的问题，提出改进YOLOv5的YOLO-EA模型，通过集成ECA和EIoU损失提升小目标与遮挡检测，精确率98.9%，召回率94.7%，实时性能达70.774fps。
摘要：
在高风险铁路施工中，个人防护装备监控至关重要，但由于目标小且经常被遮挡而具有挑战性。我们提出YOLO-EA，一种创新模型，通过将ECA集成到其骨干网络的卷积层中，增强安全措施检测能力，提高对安全帽等微小物体的辨识度。YOLO-EA通过用EIoU损失替代GIoU，进一步改善了遮挡下的目标识别。YOLO-EA的有效性通过源自真实铁路施工现场监控录像的数据集得到了实证验证。它优于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帧每秒的实时性能。这种高效且精确的YOLO-EA在复杂施工场景中具有巨大的实际应用潜力，可在复杂的铁路建设项目中强制执行严格的安全合规要求。
英文摘要：
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.
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