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Detection of Black Holes01:10

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Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
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Force Classification01:22

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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A novel object detection algorithm based on Swin Transformer.

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一种基于卷积神经网络的安全头盔检测新方法.

YueJing Qian1, Bo Wang2

  • 1Zhejiang Industry and Trade Vocational College, Wenzhou, Zhejiang, China.

PloS one
|October 13, 2023
PubMed
概括

本研究介绍了一种优化的YOLOv5模型,用于在计算能力有限的设备上有效检测安全头盔. 改进后的模型实现了更快的推断速度和更高的准确性,用于建筑安全应用.

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 工程安全工程安全工程

背景情况:

  • 为工程项目设计强大的安全头盔检测方法是具有挑战性的,因为硬件成本和移动/嵌入式设备的计算能力有限.
  • 现有的方法往往难以平衡资源有限的环境中的实时应用程序的准确性和效率.

研究的目的:

  • 开发一种优化的安全头盔检测方法,这种方法在具有有限计算能力的设备上是高效和可实现的.
  • 提高安全头盔检测系统的准确性和推断速度,用于实际的工程应用.

主要方法:

  • 优化了YOLOv5骨干网络中的瓶CSP结构,以减少模型复杂性而不改变输入/输出尺寸.
  • 设计了一个升级样本功能增强模块,以减轻升级样本期间的信息丢失,并增强语义信息.
  • 集成了一个自我注意机制,包括通道和位置注意模块,用于特征地图的自适应融合,以提高语义和位置精度.

主要成果:

  • 与相同计算能力的现有快速方法相比,拟议的方法实现了显著更快的推断速度,达到416 FPS.
  • 证明了卓越的性能,平均平均精度 (mAP) 为94.2%,表明检测精度很高.
  • 优化的模型有效地减少了复杂性,同时保持或提高了检测性能.

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结论:

  • 开发的方法为在资源有限的设备上检测安全头盔提供了高效和准确的解决方案.
  • 优化的YOLOv5模型具有增强的功能融合和注意力机制,为提高工程项目的安全性提供了实用工具.
  • 这种方法解决了计算复杂性和检测性能之间的权衡,使其适合于现实世界的部署.