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相关概念视频

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

182
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
182
Types Of Transformers01:16

Types Of Transformers

1.0K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
1.0K

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一个可靠的基再生型变压器模型用于x-小和密集的物体识别.

Ponduri Vasanthi1, Laavanya Mohan1

  • 1Vignan's Foundation for Science, Technology, and Research, Guntur, Andhra Pradesh, India.

Neural networks : the official journal of the International Neural Network Society
|July 7, 2023
PubMed
概括

这项研究引入了一个重生变压器模块,以改善检测小型和密集的物体. 这种新的方法增强了特征提取,从而在对象检测任务中获得了更高的准确性.

科学领域:

  • 计算机视觉 计算机视觉
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 深度学习模型擅长对象检测,但与x-小和密集的对象作斗争.
  • 现有的模型存在特征提取限制和框错位,导致得分位置差异.

研究的目的:

  • 为了解决检测x-小和密集物体的局限性.
  • 提出一种使用基再生型变压器模块的新型功能改进网络.

主要方法:

  • 引入了一个再生模块,以根据图像语义统计数据生成自适应性尺度.
  • 集成了一个多头自我注意 (MHSA) 变压器模块,用于深入的特征图分析.
  • 在VisDrone,VOC和SKU-110K数据集上实验验证模型.

主要成果:

  • 与现有方法相比,拟议的模型显示了更高的平均精度 (mAP),精度和回忆.
  • 在多个数据集中检测x-small和密集对象方面取得了卓越的性能.
  • 展示了基于准确性,卡帕系数和ROC指标的VOC和SKU-110K数据集的出色匹配.

结论:

  • 基于再生的变压器模块有效地克服了检测小型和密集物体的局限性.
关键词:
自动挂可以自动挂.多头自我注意的注意力.对象检测检测对象检测对象检测空间金字塔的聚合速度更快.这是YOLOv5的.

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  • 该模型能够生成特定数据集的 anchor 尺度,这大大提高了检测准确度.
  • 拟议的方法代表了对象检测在具有挑战性的场景中的重大进步.