增强的基于YOLOv8n的三模块轻量级头盔检测系统
Xinyu Zuo1, Yiqing Dai2, Chao Yu2
1School of Architecture and Design, China University of Mining and Technology, Xuzhou 221000, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
本研究提出了一种改进的YOLOv8n模型,用于高效地检测建筑安全头盔. 改进的算法提供了更高的准确性和更快的处理速度,使安全合规性更容易获得.
科学领域:
- 建筑工程安全 建筑工程安全
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 当前安全头盔检测系统面临的挑战包括低准确性和高部署成本.
- 复杂的建筑工地背景往往阻碍了现有的检测技术的性能.
研究的目的:
- 为建筑工地开发一种高效准确的安全头盔检测方法.
- 克服现有的头盔检测技术的局限性,包括准确性和成本.
主要方法:
- 一个改进的YOLOv8n模型,包含C2f-SCConv架构,部分卷积探测器 (PCD) 和坐标注意力 (CA).
- 协调注意力 (CA) 增强了检测,减少了冗余,并改善了功能本地化.
- 部分卷积探测器 (PCD) 改进了功能并降低了计算成本.
- C2f-SCConv模块取代了瓶C2f模块,以改善特征表示.
主要成果:
- 升级后的YOLOv8n模型实现了94.4%的平均精度.
- 观察到显著的改善:模型大小减少2.21 MB,率增加12.6%,FLOP减少49.9%.
- 与传统算法相比,新方法显示出更高的效率,速度和成本效益.
结论:
- 拟议的方法为现场安全头盔检测提供了更有效,更快,更具成本效益的解决方案.
- 这一进步有助于在城市建设项目中保持更安全的工作环境.
- 增强的YOLOv8n模型为改善建筑安全合规性提供了实用和可靠的工具.
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