Wu Zhihui, Lei Xiaojia, Kumar Munish
Department of Civil Engineering, Anhui Communications Vocational & Technical College, Hefei, China.
School of Petrochemical Engineering, Hunan Petrochemical Vocational and Technical College, Yueyang, China.
PLoS One. 2025 May 20;20(5):e0321713. doi: 10.1371/journal.pone.0321713. eCollection 2025.
In the context of construction site safety management, real-time object detection is crucial for ensuring workers' safety through accurate detection of safety helmets. However, traditional object detection methods often face numerous challenges in complex construction environments, such as low light, occlusion, and the diverse shapes of helmets. To address these issues, we propose an improved helmet detection model, YOLOv8-CGS, which is based on the YOLOv8 architecture and integrates optimization modules such as CBAM (Convolutional Block Attention Module), GAM (Global Attention Mechanism), and SLOU (Smooth Labeling Loss Function). The goal is to enhance the model's detection accuracy and robustness in complex scenarios. Specifically, GAM improves the model's attention to key regions, CBAM enhances its ability to perceive important features, and SLOU optimizes the accuracy of bounding box predictions, particularly in complex and occluded environments. Experimental results show that YOLOv8-CGS achieves accuracy rates of 94.58% and 92.38% on the SHD and SHWD datasets, respectively, which represent improvements of 5.9% and 5.94% compared to YOLOv8. This enhancement allows YOLOv8-CGS to provide more efficient and accurate helmet detection in practical applications, significantly improving the real-time monitoring capabilities for construction site safety management.
在建筑工地安全管理的背景下，实时目标检测对于通过准确检测安全帽来确保工人安全至关重要。然而，传统的目标检测方法在复杂的建筑环境中常常面临诸多挑战，如光线昏暗、遮挡以及安全帽形状多样等问题。为了解决这些问题，我们提出了一种改进的头盔检测模型YOLOv8 - CGS，它基于YOLOv8架构，并集成了诸如CBAM（卷积块注意力模块）、GAM（全局注意力机制）和SLOU（平滑标签损失函数）等优化模块。目的是提高模型在复杂场景下的检测精度和鲁棒性。具体而言，GAM提高了模型对关键区域的注意力，CBAM增强了其感知重要特征的能力，而SLOU优化了边界框预测的准确性，尤其是在复杂和遮挡的环境中。实验结果表明，YOLOv8 - CGS在SHD和SHWD数据集上的准确率分别达到了94.58%和92.38%，与YOLOv8相比分别提高了5.9%和5.94%。这种提升使得YOLOv8 - CGS在实际应用中能够提供更高效、准确的头盔检测，显著提高了建筑工地安全管理的实时监测能力。
