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@article{155883,
        author = {Supreetha M S and Hrushika M and Surabhi N and Pruthvi K V and Nandini B M},
        title = {Metal surface defect detection and quality evaluation using deep learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {2},
        pages = {270-276},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155883},
        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.},
        keywords = {Deep learning, Convolutional Neural Network, Random Forest, K Nearest Neighbour, Metal defect detection.},
        month = {},
        }