Hydrophobicity Classification of Composite Insulators Based on Image Enhancement and Deep Learning
Objective
The primary objective of the project titled "Hydrophobicity Classification of Composite Insulators Based on Image Enhancement and Deep Learning" is to develop an advanced and automated system for assessing the hydrophobicity status of composite insulators used in electrical power transmission and distribution networks.
Abstract
Accurately judging the hydrophobicity classes (HCs) of the composite insulator helps to grasp the antipollution flashover performance of the insulator and prevent accidents in time. Aiming at the problem of low HC recognition accuracy of hydrophobic images and interference of operators' subjectivity in the feature extraction process, an intelligent HC recognition model of composite insulators based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) image enhancement method and ResNet deep learning network is proposed in this paper. Firstly, composite insulators under varying HCs were simulated by spraying ethanol solutions with different volume fractions, and a series of hydrophobic images under experimental conditions was obtained. To improve the generalization ability of the model on photographic factors (photographing distance, light intensity, insulator color), images were preprocessed with gray processing, cropping, enhancement, and so on.
Keywords—hydrophobicity, composite insulators, image enhancement, ResNet
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is an Image/pixel?
· About image formats
· Introduction to Image Processing
· How digital image is formed
· Importing the image via image acquisition tools
· Analyzing and manipulation of image.
· Phases of image processing:
o Acquisition
o Image enhancement
o Image restoration
o Color image processing
o Image compression
o Morphological processing
o Segmentation etc.,
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skillsDemo Video
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Disclaimer - Takeoff Edu Group Projects, are not associated or affiliated with IEEE, in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, and not projects of or by IEEE.
