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相关概念视频

Force Classification01:22

Force Classification

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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.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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相关实验视频

Updated: Jul 18, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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基于多尺度融合CRNN的文本识别模型

Le Zou1, Zhihuang He1, Kai Wang1

  • 1School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China.

Sensors (Basel, Switzerland)
|August 26, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的场景文本识别模型,该模型通过多尺度融合增强了特征提取. 与传统方法相比,改进的模型通过捕捉更完整的字符特征来实现更高的准确性.

关键词:
功能融合功能融合功能多个尺度的多个尺度.文本识别功能 文本识别功能

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Deep Neural Networks for Image-Based Dietary Assessment
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相关实验视频

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Deep Neural Networks for Image-Based Dietary Assessment
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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

背景情况:

  • 场景文本识别在计算机视觉中至关重要.
  • 目前的模型由于有限的下采样尺度而难以进行不完整的特征提取.
  • 这导致识别图像中的文本的准确性降低.

研究的目的:

  • 提出一个新的场景文本识别模型,解决不完整的特征提取.
  • 通过增强功能完整性来提高文本识别准确性.
  • 在卷积循环神经网络 (CRNN) 框架内利用多尺度特征融合.

主要方法:

  • 提出了一个集结卷积,特征融合,递归和转录层的新模型.
  • 卷积层采用双尺度特征提取.
  • 一个特征融合层结合了多个尺度的特征,其次是用于上下文学习的反复层.

主要成果:

  • 拟议的模型扩大了识别领域,并在多个尺度上学习特征.
  • 它提取了更完整的字符特征,从而改善了文本识别.
  • 实验结果显示,在各种场景文本数据集上,标准CRNN模型的性能优于标准CRNN模型.

结论:

  • 新的多尺度融合CRNN模型显著提高了场景文本识别的准确性.
  • 这种方法有效地克服了现有方法中不完整的特征提取的局限性.
  • 该模型在各种现实世界场景文本数据集中展示了强大的性能.