推进建筑安全:YOLOv8-CGS头盔检测模型
Zhihui Wu1, Xiaojia Lei2, Munish Kumar3
1Department of Civil Engineering, Anhui Communications Vocational & Technical College, Hefei, China.
PloS one
|May 20, 2025
概括
本研究介绍了YOLOv8-CGS,这是一个改进的建筑安全物体检测模型. 它在具有挑战性的环境中提高了头盔检测准确度,提高了实时安全监控.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 建筑安全工程工程 建筑安全工程
背景情况:
- 实时物体检测对于建筑工地安全至关重要,特别是用于识别安全头盔.
- 传统的方法在复杂的建筑环境中扎,例如低光,遮蔽和各种各样的头盔形状.
研究的目的:
- 开发一个增强的头盔检测模型 (YOLOv8-CGS),以提高建筑安全管理的准确性和稳定性.
- 解决现有物体检测技术在具有挑战性的现实场景中的局限性.
主要方法:
- 使用YOLOv8架构作为基础.
- 综合注意力机制:卷积区注意力模块 (CBAM) 和全球注意力机制 (GAM).
- 集成的光滑标记损失函数 (SLOU) 为优化界限框预测.
主要成果:
- 在SHD数据集上,YOLOv8-CGS的准确度达到94.58%,在SHWD数据集上达到92.38%.
- 与标准YOLOv8模型相比,显示出5.9%和5.94%的显著改进.
- 在遮蔽和低光条件下检测头盔的性能提高.
结论:
- YOLOv8-CGS为实际的建筑安全应用提供了更高效,更准确的头盔检测.
- 该模型显著提高了实时监控能力,有助于提高工人安全.
- 注意模块和SLOU的集成有效地应对复杂建筑环境中的挑战.
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