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Updated: Jun 11, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Computer vision and deep transfer learning for automatic gauge reading detection.

Hitesh Ninama^1,2, Jagdish Raikwal¹, Ananda Ravuri³

¹Institute of Engineering and Technology, Devi Ahilya University, Indore, M.P., 452001, India.

Scientific Reports

|October 3, 2024

Summary

This summary is machine-generated.

This study introduces an AI system for automatic analogue gauge reading using deep learning. DenseNet 169 achieved superior precision and generalization for accurate gauge interpretation.

Keywords:

Computer vision Deep learning DenseNet 169 Gauge detection InceptionNet V3 VGG19

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Manual analogue gauge reading is prone to errors and time-consuming.
Automating gauge reading is essential for efficiency and accuracy in various industries.
Existing methods struggle with unsupervised data and achieving high precision.

Purpose of the Study:

To develop an automatic reading detection system for analogue gauges.
To leverage deep learning and image processing for enhanced gauge reading accuracy.
To compare the performance of different deep transfer learning models for this task.

Main Methods:

Utilized a combination of deep learning, machine learning, and image processing techniques.
Employed image processing to generate supervised data for training.
Trained and evaluated deep transfer learning models including DenseNet 169, InceptionNet V3, and VGG19 on 1011 labeled images.

Main Results:

DenseNet 169 demonstrated superior precision and generalization capabilities compared to InceptionNet V3 and VGG19.
VGG19 showed high training precision (97.00%) but lower testing precision (75.00%), indicating overfitting.
InceptionNet V3 exhibited consistent precision across training and testing datasets.

Conclusions:

DenseNet 169 is the most effective model for automatic analogue gauge reading detection.
The proposed system offers a robust solution for accurate and automated gauge interpretation.
AI-powered systems can significantly improve the efficiency and reliability of analogue gauge analysis.