Jove
Visualize
Contact Us

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Robust Industrial Surface Defect Detection Using Statistical Feature Extraction and Capsule Network Architectures.

Sensors (Basel, Switzerland)·2025
Same author

Optimizing Tumor Detection in Brain MRI with One-Class SVM and Convolutional Neural Network-Based Feature Extraction.

Journal of imaging·2025
Same author

Melanoma Skin Classification Using the Hybrid Approach Residual Network-Vision Transformer for Cancer Diagnosis.

Journal of clinical ultrasound : JCU·2025
Same author

Balancing Between Privacy and Utility for Affect Recognition Using Multitask Learning in Differential Privacy-Added Federated Learning Settings: Quantitative Study.

JMIR mental health·2024
Same author

Efficient Extraction of Deep Image Features Using a Convolutional Neural Network (CNN) for Detecting Ventricular Fibrillation and Tachycardia.

Journal of imaging·2023
Same author

A Smart Agricultural System Based on PLC and a Cloud Computing Web Application Using LoRa and LoRaWan.

Sensors (Basel, Switzerland)·2023
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Oct 19, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

695

Food Tray Sealing Fault Detection in Multi-Spectral Images Using Data Fusion and Deep Learning Techniques.

Mohamed Benouis1, Leandro D Medus2, Mohamed Saban2

  • 1Laboratory of Informatics and Its Applications of M'sila (LIAM), Department of Computer Science, University of M'Sila, BP 166 Ichbilia, Msila 28000, Algeria.

Journal of Imaging
|September 26, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning approach for automated food tray seal inspection using hyperspectral imaging. The method effectively detects seal faults with high accuracy, improving food safety and quality control.

Keywords:
data fusiondeep learningfault detectionfood packaginghyperspectral imaging

More Related Videos

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.2K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.5K

Related Experiment Videos

Last Updated: Oct 19, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

695
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.2K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.5K

Area of Science:

  • Food Science and Technology
  • Computer Vision
  • Machine Learning

Background:

  • Traditional food packaging inspection relies on manual operators, which is time-consuming and prone to errors.
  • Ensuring correct food tray sealing is crucial for maintaining food properties and consumer safety.
  • Hyperspectral imaging and automated vision systems offer advanced solutions for food inspection.

Purpose of the Study:

  • To develop and validate a deep learning-based approach for detecting food tray sealing faults using hyperspectral images.
  • To investigate pixel-based image fusion methods for efficient data processing.
  • To evaluate the performance of various deep learning algorithms for automated seal inspection.

Main Methods:

  • Utilized hyperspectral imaging to capture 3D image datacubes of food trays.
  • Applied pixel-based image fusion techniques to extract relevant spectral bands and create 2D images.
  • Fed the fused 2D images into deep learning (DL) algorithms, including Deep Belief Network (DBN), Extreme Learning Machine (ELM), Stacked Auto Encoder (SAE), and Convolutional Neural Network (CNN).
  • Employed an automated training process for DL algorithms, eliminating the need for manual parameter tuning.

Main Results:

  • The proposed image fusion techniques significantly reduced computation time while preserving essential information for fault detection.
  • Deep learning algorithms achieved high classification ratios for identifying faulty or normal food tray seals.
  • The Convolutional Neural Network (CNN) model demonstrated the highest accuracy at 90.1%, followed by Stacked Auto Encoder (SAE) at 89.3%, Extreme Learning Machine (ELM) at 88.3%, and Deep Belief Network (DBN) at 88.7% on the industrial dataset.

Conclusions:

  • The developed deep learning approach, combined with hyperspectral image fusion, is effective for automated food tray sealing fault detection.
  • The method enhances inspection efficiency and accuracy compared to traditional manual inspection.
  • This technology holds significant potential for improving food safety and quality control in the food industry.