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相关概念视频

Masonry in Cold and Hot Weather Conditions01:21

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In cold weather, masonry construction requires specific precautions to ensure mortar does not freeze before curing, as this can significantly weaken its strength and watertightness. Mortar temperature should be maintained between 60°F and 80°F to support proper hydration and curing. Below 40°F, mortar water must be heated, but should not exceed 120°F as high temperatures can reduce mortar's compressive and bond strength.
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The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
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相关实验视频

Updated: Jan 15, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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一个自我注意力驱动的多尺度物体检测框架,用于智能城市的恶劣天气.

Batti Tulasi Dasu1,2, M Vijay Reddy1, Koppula Vijaya Kumar3

  • 1Department of Computer Science and Engineering, GIET University, Gunpur, Odisha, India.

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|January 13, 2026
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这项研究引入了在恶劣天气中强大的物体检测的新框架,提高了道路安全和城市安全. 综合系统在具有挑战性的环境条件下显著提高了检测准确度和可靠性.

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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
  • 智慧城市技术 智慧城市技术

背景情况:

  • 对象检测系统在恶劣天气 (雾,雨,雪) 中扎,导致性能下降.
  • 降低可见度和背景噪音模糊了对象的界限,影响了道路安全和城市安全.

研究的目的:

  • 提出一个自我注意力驱动的多尺度物体检测框架,以在恶劣的天气条件下可靠地检测物体.
  • 在具有挑战性的环境场景中提高现有物体检测模型的性能.

主要方法:

  • 集成管道结合了自适应双背景模型 (ADBM),基于像素的有限状态机器 (PFSM) 和基于注意力的尺度模块 (ASM) 与自我注意力优化你只看一次 (SAO-YOLO).
  • 背景精细化,特征选择和注意力引导检测之间的整体相互作用,以实现稳定,上下文意识的预测.
  • 在特征金字塔网络 (FPN) 的 P3 级别进行热视觉特征融合,以提高可见性强度.

主要成果:

  • 实现了97.25%的准确性,95.33%的精确性,96.37%的回忆力和96.55%的F1分数在不利天气自然数据集中的检测.
  • 超越了包括YOLOv5,YOLOv8和DETECTION TRansformer在内的最先进的模型.
  • 在复杂的天气变化下证明了卓越的检测可靠性.

结论:

  • 集成的ADBM-PFSM-ASM-SAO-YOLO框架有效地在恶劣天气中保持高检测可靠性.
  • 拟议的模型为实时交通监控,城市监控和公共安全应用提供了实用的解决方案.
  • 该研究强调了综合方法在不确定的环境中进行强大的物体检测的重要性.