水利水电技术（中英文）

随着现代建筑工程的复杂度不断提升，高空作业等特殊施工场景的安全风险日益突出。近年来，由于施工人员未佩戴安全帽引发的高空坠落、物体打击等事故频繁发生，这些事故不仅对施工人员的生命安全构成了严重威胁，同时也对施工进度和工程质量造成严重影响。为了有效解决这一问题，基于施工现场图像数据构建了一个安全帽佩戴检测数据集，提取了不同环境下施工人员的佩戴特征，并针对这一数据集，设计并应用了基于YOLOv5的施工现场安全帽佩戴检测模型。该模型将施工人员和安全帽作为检测目标，通过网络训练与分类性能评估，所提出的模型在测试中取得了89.8%的检测精度。该方法不仅实现了全天候、全覆盖的自动化监管，极大提高了检测效率，而且有效降低了人工监管的成本。试验结果表明，该系统具备良好的鲁棒性，能够在复杂和多变的施工现场环境中稳定运行，满足施工现场安全管理的需求。

With the increasing complexity of modern construction projects, safety risks in high-altitude operations and other special construction scenarios have become more prominent, where frequent accidents not only severely threaten workers' lives but also significantly impact construction progress and project quality. To address these safety challenges in complex environments, a safety helmet detection dataset was developed using construction site images that captures workers' wearing characteristics under various conditions, and proposed an improved YOLOv5-based detection model that specifically targets both workers and helmets while enhancing small-object detection capability for high-altitude scenarios. Through network training and performance evaluation, the model achieved 89.8% detection accuracy, demonstrating its effectiveness in enabling automated, comprehensive monitoring that significantly improves inspection efficiency while reducing safety risks and costs associated with manual high-altitude supervision. Experimental result confirm the system's strong robustness in operating reliably within complex and dynamic high-risk construction environments, meeting critical safety management requirements for specialized construction operations.