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相关概念视频

Aggregates Classification01:29

Aggregates Classification

305
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
305
Classification of Systems-I01:26

Classification of Systems-I

169
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
169
Classification of Signals01:30

Classification of Signals

403
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
403
Classification of Systems-II01:31

Classification of Systems-II

134
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
134
Methods of Classification and Identification01:28

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
Force Classification01:22

Force Classification

1.1K
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,...
1.1K

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相关实验视频

Updated: Jun 7, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

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香和果数据集用于基于机器学习和深度学习的质量分类.

Abiban Kumari1, Jaswinder Singh1

  • 1Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar 125001, Haryana, India.

Data in brief
|November 11, 2024
PubMed
概括

介绍了一套新的数据集,用于使用机器学习对香和瓜质量进行分类. 该资源解决了有限数据的挑战,使得先进的水果分类模型的开发成为可能.

科学领域:

  • 农业科学 农业科学
  • 计算机科学 计算机科学

背景情况:

  • 准确的水果识别和分类对于可持续的农业和园艺至关重要.
  • 机器学习 (ML) 为水果分类提供了先进的技术,但需要全面的数据集.
  • 果实数据集的有限可用性是开发强大的ML模型的一个重大挑战.

研究的目的:

  • 为水果分类提供一个全面的数据集,特别是对于香和瓜.
  • 用非破坏性方法根据质量对水果进行分类.
  • 支持开发用于水果质量评估的ML模型.

主要方法:

  • 使用Redmi Note 10-Pro移动摄像头收集了香和果图像的数据集.
  • 图像是从各种角度在自然阳光下拍摄的.
  • 数据集根据生理变化被分为三类:A类,B类和缺陷.

主要成果:

  • 成功创建了香和果质量分类的综合数据集.
  • 数据集包括按质量 (A类,B类) 和缺陷分类的图像.
  • 这些数据适用于训练和验证机器学习模型.

结论:

关键词:
计算机视觉 计算机视觉 计算机视觉深度学习是一种深度学习.水果的分类方法 水果的分类方法图像处理 图像处理机器学习 机器学习

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  • 开发的数据集有助于创建有效的ML模型,用于水果质量分类.
  • 这种资源可以通过快速准确的分类来帮助水果储存,加工和出口行业.
  • 这一数据集的可用性促进了水果质量评估技术的进步.