Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jan 13, 2026

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

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

Published on: December 15, 2023

Edge-Embedded Multi-Feature Fusion Network for Automatic Checkout.

Jicai Li¹, Meng Zhu², Honge Ren^1,3

¹College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.

Journal of Imaging

|October 28, 2025

Summary

This summary is machine-generated.

Related Concept Videos

Extraction: Advanced Methods

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...

Force Classification

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

You might also read

Related Articles

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

Sort by

Same journal

Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.

Journal of imaging·2026

Same journal

Coronary Artery Anomalies and Anatomical Variants: Cross-Sectional Diagnostic Imaging and Clinical Background.

Journal of imaging·2026

Same journal

YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.

Journal of imaging·2026

Same journal

Radiomics-Guided Multi-Sequence Learning for Pathological Complete Response Prediction from Breast MRI with Missing Auxiliary Sequences.

Journal of imaging·2026

Same journal

Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.

Journal of imaging·2026

Same journal

Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology.

Journal of imaging·2026

See all related articles

This study introduces a new network for automatic checkout systems, improving shopping list accuracy from images. The Edge-Embedded Multi-Feature Fusion Network (E2MF2Net) enhances product detection, especially with occlusions.

Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Automatic Checkout (ACO) systems require accurate product recognition from images.
Challenges include product occlusions, numerous categories, and cluttered layouts, demanding robust detection models.
Existing models struggle with generalization and robustness in real-world checkout scenarios.

Purpose of the Study:

To develop a novel network, the Edge-Embedded Multi-Feature Fusion Network (E2MF2Net), for accurate shopping list generation from checkout images.
To enhance the robustness and generalization capabilities of detection models in ACO tasks.
To improve synthetic image generation for better training data.

Main Methods:

Proposed the Edge-Embedded Multi-Feature Fusion Network (E2MF2Net) for joint optimization of synthetic image generation and feature modeling.

Keywords:

automatic checkout edge enhancement multi-feature fusion object detection

Related Experiment Videos

Last Updated: Jan 13, 2026

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

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

Published on: December 15, 2023

Introduced Hierarchical Mask-Guided Composition (HMGC) for photorealistic synthetic image generation with occlusion tolerance.

Incorporated an Edge-Embedded Enhancement Module (E3) and Multi-Feature Fusion Module (MFF) for improved feature extraction and fusion.

Main Results:

E2MF2Net achieved state-of-the-art checkout accuracy (cAcc) on the RPC dataset: 98.52% (Easy), 97.95% (Medium), 96.52% (Hard), and 97.62% (Average).
Demonstrated a significant improvement of 3.63 percentage points in the Hard mode, indicating superior performance with occluded products.
Showcased strong robustness and adaptability in incremental learning and domain generalization scenarios.

Conclusions:

E2MF2Net effectively addresses the challenges of product occlusion and complex layouts in automatic checkout tasks.
The proposed network significantly improves the accuracy and robustness of shopping list generation from checkout images.
The method shows promise for real-world deployment of advanced automatic checkout systems.