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相关概念视频

Detection of Black Holes01:10

Detection of Black Holes

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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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相关实验视频

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在船舶区域使用深度学习和计算机视觉方法的火灾检测和通报方法.

Kuldoshbay Avazov1, Muhammad Kafeel Jamil1, Bahodir Muminov2

  • 1Department of Computer Engineering, Gachon University, Seongnam-si 461-701, Republic of Korea.

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|August 26, 2023
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概括

这项研究引入了使用YOLOv7深度学习的船舶先进的火灾检测系统,达到93%的准确性. 该技术通过实时识别和减轻火灾来提高海上安全.

关键词:
您可以使用E-ELAN.这就是YOLOv7的意思.深度学习是一种深度学习.火的火的火的火的火火焰检测检测器的火焰检测器船只船只的船只船只的船只

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科学领域:

  • 海上安全的航行.
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 船舶火灾对船员,货物和环境构成严重风险.
  • 及时检测火灾对于有效缓解和响应至关重要.

研究的目的:

  • 开发和评估用于海上环境的新型火灾检测系统.
  • 利用深度学习,特别是YOLOv7,提高火灾识别能力.

主要方法:

  • 使用了YOLOv7和改进的E-ELAN骨干来检测火灾.
  • 在4622个船舶场景的增强图像上训练模型.
  • 使用标准指标评估模型性能,并与现有方法进行比较.

主要成果:

  • 在火灾检测方面实现了93%的准确率.
  • 与前相比,展示了优越的功能融合和识别能力.
  • 展示了在具有挑战性的海上条件下实时检测的潜力.

结论:

  • 拟议的基于YOLOv7的系统显著提高了海上消防安全.
  • 该模型有效用于实时火灾检测和船舶环境中的小物体识别.
  • 这种深度学习方法为船舶保护和港口火灾监测提供了有希望的解决方案.