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

Related Concept Videos

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
There are three main types of inductively coupled plasma atomic emission spectroscopy (ICP-AES) instruments: sequential, simultaneous multichannel, and Fourier transform instruments, with the latter being less commonly used....

You might also read

Related Articles

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

Sort by

Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Computational intelligence and neuroscience·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 6, 2025

Multimodal Optical Imaging Platform for Studying Cellular Metabolism

Multimodal Optical Imaging Platform for Studying Cellular Metabolism

Published on: June 6, 2025

MSIS: Multispectral Instance Segmentation Method for Power Equipment.

Jun Shu¹, Juncheng He², Ling Li²

¹School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China.

Computational Intelligence and Neuroscience

|January 14, 2022

Summary

This summary is machine-generated.

This study introduces a multispectral instance segmentation (MSIS) model for improved power equipment fault detection. The novel approach effectively segments power equipment in infrared images, overcoming challenges like low contrast and complex backgrounds.

More Related Videos

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Related Experiment Videos

Last Updated: Oct 6, 2025

Multimodal Optical Imaging Platform for Studying Cellular Metabolism

Multimodal Optical Imaging Platform for Studying Cellular Metabolism

Published on: June 6, 2025

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Area of Science:

Electrical Engineering
Computer Vision
Artificial Intelligence

Background:

Infrared thermography is crucial for power equipment fault detection.
Existing segmentation methods struggle with overlapping equipment, complex backgrounds, and low contrast in infrared images.
Accurate segmentation is vital for reliable thermal fault detection.

Purpose of the Study:

To develop an advanced method for segmenting power equipment in infrared images.
To improve the accuracy and robustness of power equipment detection and segmentation.
To address the limitations of current methods in handling challenging infrared image conditions.

Main Methods:

A novel multispectral instance segmentation (MSIS) model based on SOLOv2 was designed.
The model incorporates a new multispectral feature extraction structure for visible and infrared images.
A Feature Fusion module (MARFN) was developed for enhanced feature integration.

Main Results:

The proposed MSIS model demonstrated excellent performance in power equipment instance segmentation.
The MSIS model achieved 40.06% Average Precision (AP) when based on ResNet-50.
The integrated approach effectively combines multispectral features for superior segmentation outcomes.

Conclusions:

The developed MSIS model significantly enhances the segmentation of power equipment in infrared images.
This method offers a promising solution for more accurate and reliable power equipment thermal fault detection.
The study highlights the effectiveness of combining multispectral information and advanced instance segmentation techniques.