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

You might also read

Related Articles

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

Sort by
Same author

Integrated anthropometric correlates of planned change-of-direction performance (T-test) in male badminton players: a partial least squares regression study.

Frontiers in physiology·2026
Same author

Concurrent control of natural and robotic limbs through a tactile-encoded brain-computer interface.

Nature communications·2026
Same author

Formation of Bimetallic Nanoparticles via Exsolution Using a Reducible Metal Oxide Capping Layer.

ACS nano·2026
Same author

Jasmonate, salicylate, and ethylene-responsive transcriptomics discovery in spikelets of three wheat genotypes reveals a rapid and conserved response for jasmonate signaling.

Plant signaling & behavior·2026
Same author

ALDOC modulates astrocytic glycolysis and AMPK/mTOR/HIF-1α signaling in Alzheimer's disease.

Frontiers in neuroscience·2026
Same author

Assessing different Protein A resins' homodimer separation potentials through processing a two-antibody-containing artificial mixture.

Protein expression and purification·2026

Related Experiment Video

Updated: Nov 19, 2025

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping
12:19

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping

Published on: December 8, 2015

12.7K

A robust identification method for nonferrous metal scraps based on deep learning and superpixel optimization.

Yifeng Li1,2,3, Xunpeng Qin1,2,3, Zhenyuan Zhang1,2

  • 1School of Automotive Engineering, Wuhan University of Technology, People's Republic of China.

Waste Management & Research : the Journal of the International Solid Wastes and Public Cleansing Association, ISWA
|January 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method for identifying aluminum and copper scraps from end-of-life vehicles. The technique uses a convolutional neural network and SEEDS to improve metal recycling and resource management.

Keywords:
Recycleclassificationconvolutional neural networknonferrous metal scrapssuperpixel

More Related Videos

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

801
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.4K

Related Experiment Videos

Last Updated: Nov 19, 2025

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping
12:19

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping

Published on: December 8, 2015

12.7K
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

801
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.4K

Area of Science:

  • Materials Science
  • Computer Science
  • Environmental Science

Background:

  • End-of-life vehicles (ELVs) are a significant source of metal resources.
  • Current recycling methods struggle with automatic classification of nonferrous metal scraps like aluminum (Al) and copper (Cu).
  • Efficient separation of nonferrous metals is crucial for resource utilization and sustainability.

Purpose of the Study:

  • To develop an automated identification method for nonferrous metal scraps (Al and Cu).
  • To enhance the separation and management of recycled metal resources.
  • To increase the sustainability of metal recycling processes.

Main Methods:

  • Utilized a convolutional neural network (CNN) combined with SEEDS (superpixels extracted via energy-driven sampling).
  • Employed SEEDS for training patch generation, image data augmentation, and automatic labeling.
  • Applied SEEDS to optimize CNN outputs for improved accuracy.

Main Results:

  • Achieved an average precision of 0.98 across 15 test samples in diverse environments.
  • Demonstrated robust performance in identifying aluminum and copper scraps.
  • The model proved effective even with practical challenges in classification.

Conclusions:

  • The proposed CNN and SEEDS model offers a robust solution for automatic nonferrous metal scrap identification.
  • This method can be scaled to complex industrial recycling environments.
  • Presents new possibilities for highly accurate automated classification, enhancing metal resource management and sustainability.