用改进YOLOv8实现实时工地安全帽检测，提升准确率与效率。
CIB-SE-YOLOv8: Optimized YOLOv8 for Real-Time Safety Equipment Detection on Construction Sites
- 引入SE注意力与优化的C2f模块增强特征提取
- 在SHEL5K数据集上实现更高检测精度与实时性能
- 适合工地安全管理、智能监控系统开发者参考
确保工地安全至关重要，安全帽在减少伤害中起关键作用。传统安全检查耗时且常不到位。本研究提出基于计算机视觉的实时安全帽检测方案，采用YOLO框架并利用SHEL5K数据集。所提出的CIB-SE-YOLOv8模型融合了SE注意力机制与改进的C2f结构，提升了检测精度与运行效率。该方法为促进工地安全合规提供了更有效的技术手段。
原文摘要 · Abstract (English)
Ensuring safety on construction sites is critical, with helmets playing a key role in reducing injuries. Traditional safety checks are labor-intensive and often insufficient. This study presents a computer vision-based solution using YOLO for real-time helmet detection, leveraging the SHEL5K dataset. Our proposed CIB-SE-YOLOv8 model incorporates SE attention mechanisms and modified C2f blocks, enhancing detection accuracy and efficiency. This model offers a more effective solution for promoting safety compliance on construction sites.
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