一个改进的YOLOv8安全头盔戴着检测网络的检测网络
Xudong Song1, Tiankai Zhang1, Weiguo Yi2
1Computer and Communication Engineering Institute, Dalian Jiaotong University, 794 Huanghe Road, Shahekou District, Dalian, Liaoning, China.
Scientific reports
|July 30, 2024
概括
这项研究引入了改进的YOLOv8网络,用于在工业环境中检测安全头盔的佩戴. 改进后的模型显著提高了准确性,解决了诸如小目标和复杂背景等挑战.
科学领域:
- 工业安全工程 工业安全工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 在工业环境中,工人的安全至关重要,安全帽的使用至关重要.
- 由于复杂的背景和不同距离,检测佩戴头盔是具有挑战性的.
- 现有的方法在错误检测和忽略小目标方面扎.
研究的目的:
- 开发一个先进的YOLOv8网络,用于准确的安全头盔佩戴检测.
- 解决检测小目标的局限性,提高复杂工业环境中的性能.
主要方法:
- 一个改进的YOLOv8网络,包含一个Dilation-wise剩余注意模块.
- 集成的atrous空间金字塔聚合用于增强的多尺度特征处理.
- 使用规范化的瓦瑟斯坦距离损失函数,以提高检测准确度.
主要成果:
- 改进的网络在SHWD数据集上实现了92.0%的平均平均精度 (mAP).
- 与传统方法相比,证明了卓越的准确性,回忆和检测能力.
- 在复杂的工业环境中成功增强了对戴头盔的检测.
结论:
- 提议的改进的YOLOv8网络有效地提高了安全头盔佩戴的检测准确性.
- 该网络的架构非常适合应对复杂工业环境中的挑战.
- 这一进步有助于通过更可靠的检测系统提高工人安全.
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