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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.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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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,
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Updated: Jan 1, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Published on: December 15, 2023

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Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance

Naveed Ilyas1, Ahsan Shahzad2, Kiseon Kim1

  • 1School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea.

Sensors (Basel, Switzerland)
|December 22, 2019
PubMed
Summary
This summary is machine-generated.

Intelligent crowd-counting uses machine learning and AI for better crowd management. Convolutional neural networks show promise for analyzing crowd density despite challenges like occlusion and scale variations.

Keywords:
crowd analysisdeep learningsmart cities

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional crowd-counting methods are being replaced by AI and machine learning.
  • This shift enables adaptive monitoring and control of dynamic crowd gatherings.
  • Current challenges include occlusion, clutter, and scale variations in crowd images.

Purpose of the Study:

  • To review, categorize, and analyze Convolutional Neural Network (CNN)-based crowd-counting techniques.
  • To evaluate the performance of the latest CNN crowd-counting methods.
  • To highlight potential applications and future research directions for CNN-based crowd counting.

Main Methods:

  • Review and categorization of existing CNN-based crowd-counting literature.
  • Detailed performance evaluation of selected CNN techniques.
  • Analysis of limitations and distinctive features of various approaches.

Main Results:

  • Convolutional Neural Networks (CNNs) are identified as a promising technology for intelligent crowd counting and analysis.
  • A comprehensive evaluation of the latest CNN-based crowd-counting techniques is provided.
  • Potential applications and future research avenues are discussed.

Conclusions:

  • CNNs offer advanced capabilities for adaptive monitoring and management of crowd gatherings.
  • Addressing challenges like occlusion and scale variation is crucial for improving CNN-based crowd counting.
  • Future research should focus on enhancing CNN designs for robust crowd analysis, including smart city applications with the Internet of Things (IoT).