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使用GPD-YOLOv8算法在工作场所安全佩戴的轻量级检测模型
Jian Xing1, Chenglong Zhan2, Jiaqiang Ma1
1School of Electronic Information Technology, Northeast Forestry University, Harbin, 150040, People's Republic of China.
Scientific reports
|January 8, 2025
概括
这项研究引入了改进的YOLOv8模型用于建筑安全检测磨损,提高了准确性和速度. 该模型确保正确使用头盔和反射服装,显著减少工作场所的风险.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 职业安全 在职业安全.
背景情况:
- 由于不适当使用个人防护设备 (PPE),建筑工地存在高安全风险.
- 准确有效地检测安全服装,如头盔和反射服装,对于工人的安全至关重要.
研究的目的:
- 开发一个增强的YOLOv8模型,以改善建筑工地安全磨损检测.
- 提高检测头盔和反射服装的准确性和速度.
主要方法:
- 整合一个P2检测层来丰富语义特征表示.
- 整合了一个轻量级的Ghost模块,以减少计算负载.
- 添加了一个动态头 (Dyhead) 与一个注意力机制的特征提取.
- 采用指数移动平均线 (EMA) SlideLoss函数,以提高准确性和稳定性.
主要成果:
- 拟议的模型在平均平均精度 (mAP) 中比基线YOLOv8.2取得了6.2%的改善.
- 检测速度以每秒 (FPS) 增加了55.88%.
- 增强型号在检测安全穿戴方面表现出卓越的性能.
结论:
- 增强的YOLOv8模型通过准确检测安全穿戴,有效地解决安全风险.
- 该模型在施工现场安全应用中,在检测准确度和速度上都提供了显著的改进.
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