Metal surface defect detection and quality evaluation using deep learning
Supreetha M S, Hrushika M, Surabhi N, Pruthvi K V, Nandini B M
Deep learning, Convolutional Neural Network, Random Forest, K Nearest Neighbour, Metal defect detection.
Detecting defects on metal surface is vital for businesses to preserve quality measures of the item and to help excess in generation. With this work, we put forward three machine learning (ML) classifiers- Convolutional Neural Network, Random Forest, K Nearest Neighbour to distinguish, detect and classify the deformity and defect within the dataset. Firstly, information is pre-processed to format images. At that point the models are utilized to train defect detection classification assignment with finest combination of weights and bias to ML calculation. Besides, quality evaluation is done among the three models with the assistance of diverse criteria from classification report.
Article Details
Unique Paper ID: 155883

Publication Volume & Issue: Volume 9, Issue 2

Page(s): 270 - 276
Article Preview & Download

Share This Article

Conference Alert


AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2023

SWEC- Management


Last Date: 7th November 2023

Go To Issue

Call For Paper

Volume 10 Issue 1

Last Date for paper submitting for March Issue is 25 June 2023

About Us enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on

Social Media

Google Verified Reviews