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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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相关实验视频

Updated: Jul 12, 2025

A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact
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研究基于YOLOv5的改进的头盔检测算法.

Chun Shan1,2, HongMing Liu3, Yu Yu3

  • 1Guangdong Polytechnic Normal University, Guangzhou, China. shanchun@gpnu.edu.cn.

Scientific reports
|October 23, 2023
PubMed
概括
此摘要是机器生成的。

这项研究使用改进的YOLOv5模型增强了安全头盔检测,达到95.9%的准确性. 这种先进的方法减少了复杂工业环境中的错误检测,提高了工人安全.

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 工业安全 工业安全 工业安全

背景情况:

  • 智能工业园区需要强大的安全头盔检测.
  • 现有的方法与小,密集的目标作斗争,导致错误报警和错过检测.

研究的目的:

  • 增强YOLOv5目标检测算法,以实时检测安全头盔.
  • 在复杂的工业环境中提高准确性和减少错误.

主要方法:

  • 在YOLOv5骨干中内置ECA通道注意力机制,以实现高效的特征提取.
  • 使用加权双向特征金字塔网络 (BiFPN) 进行有效的特征融合.
  • 引入了脱头,以提高检测性能和融合.

主要成果:

  • 改进的YOLOv5模型在定制头盔数据集上实现了95.9%的平均准确性.
  • 与原来的YOLOv5.5相比,显示了3.0个百分点的精度增加.
  • 在复杂的场景中检测安全头盔合规性的显著改进.

结论:

  • 经过修改的YOLOv5型号为安全头盔检测提供了卓越的性能.
  • 这种方法提高了准确性和稳定性,这对于工业安全应用至关重要.
  • 该研究为智能工业园区的实时监控提供了更可靠的解决方案.