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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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Microcracking in concrete refers to the tiny cracks that can form within the material even before any external load is applied. These microcracks typically occur at the interface between the coarse aggregate and the hydrated cement paste, often as a result of differential volume changes prompted by variations in stress-strain behavior, as well as thermal and moisture movement. Initially, these microcracks remain stable and do not grow substantially until the concrete is stressed to about 30...
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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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相关实验视频

Updated: Jul 24, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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基于深度学习的岩石裂纹识别技术

Jinbei Li1, Yu Tian2, Juan Chen2

  • 1School of Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种深度学习方法,用于使用无人机成像检测岩石裂,改善地质灾害预测. 增强的YOLOv7模型与SimAM注意力实现100%的精度,为山体滑坡和塌提供快速和准确的早期预警.

关键词:
这就是YOLOv7的意思.关注注意力注意力注意力注意力破裂 破裂 破裂 破裂 破裂这是一场灾难性的灾难.对象检测检测对象检测对象检测

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科学领域:

  • 地质工程是地质工程.
  • 计算机科学 计算机科学
  • 遥感 遥感 遥感 遥感

背景情况:

  • 岩层表面的裂是地质灾害的关键早期指标,如山体滑坡和崩.
  • 准确和快速的裂检测对于有效地监测和缓解地质灾害至关重要.
  • 无人机视频摄影提供了独立于地形的解决方案,用于捕获表面裂数据.

研究的目的:

  • 开发和评估基于深度学习的岩石裂识别技术,用于早期地质灾害检测.
  • 增强YOLOv7模型的注意力机制,以提高裂识别的准确性和效率.
  • 利用无人机成像建立一种新的方法,用于精确和快速地分析岩石表面裂.

主要方法:

  • 无人机获得的岩石表面图像被细分成640x640像素的补丁.
  • 使用数据增强和Labelimg用于裂物体检测,创建了一个VOC数据集.
  • 通过整合各种注意力机制来修改YOLOv7模型,包括SimAM.
  • 数据被分为训练 (80%) 和测试 (20%) 组,用于模型评估.

主要成果:

  • 使用SimAM注意力机制改进的YOLOv7模型实现了100%的精度,75%的回忆率和96.89%的AP.
  • 这种优化的模型仅在10秒内处理了100张图像,超过了其他五种模型.
  • 与原来的YOLOv7相比,改进后的模型在精度上提高了1.67%,在回忆中提高了1.25%,在AP上提高了1.45%,而不影响速度.

结论:

  • 基于深度学习的岩石裂识别技术能够快速准确地识别地质危险前体.
  • YOLOv7与SimAM注意力机制的整合为岩石裂检测提供了一个高度有效的解决方案.
  • 这项研究为地质灾害的预警系统提供了新的方向,提高了公共安全.