Jove
Visualize
Contact Us
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 Concept Videos

Aggregates Classification01:29

Aggregates Classification

1.2K
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...
1.2K
Optimal Foraging00:48

Optimal Foraging

14.3K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
14.3K

You might also read

Related Articles

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

Sort by
Same author

Cross-modal recipe retrieval via multi-granularity alignment.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

A comparative study of vision-language models for food ingredient recognition and nutrient estimation.

Current research in food science·2026
Same author

Hybrid Decoding with Co-Occurrence Awareness for Fine-Grained Food Image Segmentation.

Foods (Basel, Switzerland)·2026
Same author

Multi-View Edge Attention Network for Fine-Grained Food Image Segmentation.

Foods (Basel, Switzerland)·2025
Same author

Lipid on stroke in intracranial artery atherosclerotic stenosis: a mediation role of glucose.

Frontiers in endocrinology·2024
Same author

Phonological awareness and RAN contribute to Chinese reading and arithmetic for different reasons.

Cognitive processing·2024

Related Experiment Video

Updated: Apr 16, 2026

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.2K

Lightweight Food Localization and Recognition via Multi-Branch Feature Learning and Enhanced Aggregation.

Xiangyi Zhu, Yancun Yang, Pindan Cao

    IEEE Journal of Biomedical and Health Informatics
    |April 14, 2026
    PubMed
    Summary
    This summary is machine-generated.

    A new YOLO-Multi Feature Fusion model enhances food image recognition on edge devices. This lightweight approach improves accuracy and efficiency for dietary monitoring and health management applications.

    Related Experiment Videos

    Last Updated: Apr 16, 2026

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

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    10.2K

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Food Computing

    Background:

    • Food image localization and recognition are crucial for dietary monitoring on edge devices.
    • Challenges include high intra-class variability, inter-class similarity, and non-rigid food characteristics.

    Purpose of the Study:

    • To propose a novel multi-feature fusion model, YOLO-Multi Feature Fusion, for improved food image localization and recognition on edge devices.
    • To enhance accuracy while reducing model parameters and computational load.

    Main Methods:

    • The YOLO-Multi Feature Fusion model integrates Ghost Bottleneck, a Multi-Scale Feature Bottleneck, a Bidirectional Vision Transformer, and an Information Cross-Exchange module.
    • The model is built upon the YOLOv5 framework, optimizing feature capture and fusion.

    Main Results:

    • YOLO-Multi Feature Fusion demonstrated superior performance compared to existing lightweight detectors on UEC Food100, UEC Food256, and ZSFooD datasets.
    • Achieved mAP improvements of 3.0%, 3.0%, and 0.3% respectively, with significant reductions in model parameters and computational load.

    Conclusions:

    • YOLO-Multi Feature Fusion effectively addresses challenges in food image analysis for edge computing.
    • The model offers a promising solution for efficient and accurate dietary monitoring and health management systems.