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Updated: Dec 21, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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A deep learning and image-based model for air quality estimation.

Qiang Zhang1, Fengchen Fu1, Ran Tian1

  • 1College of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu Province, 730070, China.

The Science of the Total Environment
|May 16, 2020
PubMed
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This study introduces AQC-Net, a deep learning model using scene images for air quality estimation. It achieves superior accuracy in air quality classification compared to other methods, aiding pollution control efforts.

Area of Science:

  • Environmental Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Air pollution poses a significant threat to public health, necessitating effective air quality monitoring.
  • Timely air quality data is crucial for pollution control strategies and safeguarding human health.

Purpose of the Study:

  • To propose a novel deep learning and image-based model for accurate air quality estimation.
  • To enhance feature representation and improve model performance through a self-supervision module (SCA).

Main Methods:

  • Developed AQC-Net, a deep learning model that extracts features from scene images for air quality classification.
  • Integrated a self-supervision module (SCA) to reconstruct features using channel map interdependence, improving feature representation.
Keywords:
Convolutional neural networkParticipatory sensingPublic satisfactionResidual network

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Last Updated: Dec 21, 2025

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.8K
  • Utilized a newly compiled high-quality outdoor air quality dataset (NWNU-AQI) for training and evaluation.

    • Main Results:

    • AQC-Net demonstrated superior accuracy in air quality classification compared to Support Vector Machine (SVM) and Deep Residual Network (ResNet) on the NWNU-AQI dataset.
    • The self-supervision module (SCA) effectively enhanced feature representation and model performance.
    • The NWNU-AQI dataset provides a valuable resource for evaluating air quality estimation models.

    Conclusions:

    • The proposed AQC-Net model offers a promising approach for accurate, image-based air quality estimation.
    • Deep learning, particularly with self-supervision, can significantly improve air quality monitoring capabilities.
    • The developed NWNU-AQI dataset facilitates advancements in the field of environmental monitoring using AI.