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相关概念视频

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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相关实验视频

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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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优化深度学习视觉系统,用于从无人机图像中识别人类行为.

Hussein Samma1, Ali Salem Bin Sama2

  • 1SDAIA-KFUPM Joint Research Center for Artificial Intelligence (JRC-AI), King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.

Multimedia tools and applications
|June 26, 2023
PubMed
概括

本研究介绍了一种轻量级的计算机视觉系统,使用优化的SqueezeNet骨干和双层粒子群优化器 (TLPSO) 来有效地识别来自无人机图像的人类行为. 该系统实现了七倍的速度增加,而不会损失精度.

关键词:
深度学习是一种深度学习.人类行动的认可 人类行动的认可优化算法的优化算法这就是SqueezeNet.这是一个YOLO YOLO.

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 优化算法 优化算法

背景情况:

  • 像YOLO这样的深度学习视觉系统经常使用计算上昂贵的骨干网络 (例如ResNet,Inception).
  • SqueezeNet提供了一个压缩的替代方案,但被训练用于广泛的对象分类,而不是特定的动作识别.
  • 优化特征提取对于高效,硬件有限的视觉系统至关重要.

研究的目的:

  • 开发一种轻量级的视觉系统,用于从无人机图像中识别人类行为.
  • 优化SqueezeNet骨干,使用一种新的算法来提高效率和准确性.
  • 评估系统在识别步行和跑步行为方面的表现.

主要方法:

  • 集成一个双层粒子群集优化器 (TLPSO) 与YOLO和Squeeze.Net.
  • 使用TLPSO来减少SqueezeNet用于人类行动识别的卷积过器.
  • 数据集包括300个无人机图像 (100个跑步,200个行走) 在各种条件下.

主要成果:

  • TLPSO将SqueezeNet过器减少了52%,从而使检测速度增加了七倍.
  • 获得了94.65%的F1得分,推断时间为0.061毫秒.
  • 与PSO和RLMPSO相比,TLPSO表现出更好的趋同性和适应性.

结论:

  • 拟议的轻量级视觉系统有效地识别无人机录像中的人类行为,准确性和速度高.
  • TLPSO是一种高效的算法,用于优化轻量级网络中的卷积过器.
  • 该系统的性能优于以前基于无人机的人类行为识别的方法.