Metal surface defect detection and quality evaluation using deep learning

Metal surface defect detection and quality evaluation using deep learning
Author(s):
Supreetha M S, Hrushika M, Surabhi N, Pruthvi K V, Nandini B M
Keywords:
Deep learning, Convolutional Neural Network, Random Forest, K Nearest Neighbour, Metal defect detection.
Abstract
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
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NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

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