SURFACE DEFECT DETECTION SYSTEM WITH MACHINE LEARNING

SIRITH N; SUBRAMANYA N S; SRINIVAS K R; NISARGA V; VIJAYANANDA

SURFACE DEFECT DETECTION SYSTEM WITH MACHINE LEARNING

June 2022

Vol-8, Issue-3

Paper ID: 17619

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Information Science Engineering

Keywords

Surface Defect Machine Learning Classification Random Forest Convolution neural network.

Abstract

Surface quality is the essential parameter for a product. In an industry, manual defect inspection is a tedious assignment. Consequently, it is difficult to guarantee the surety of a flawless steel surface. To meet user requirements, speed up the inspection process, and to improve the overall efficiency of the industry, Machine Learning based automatic surface investigation strategies have been proven to be exceptionally powerful and prevalent solution in the recent years. We have taken a traditional machine learning approach to resolve this problem. This project makes an attempt to enhance the performance of the model using Image preprocessing techniques and use these extracted features to train and build a machine learning model to segment and classify defect images. The input is taken from the NEU surface defect database. This database contains six types of defects including crazing, inclusion, patches, pitted surface, rolled-in-scale, and scratches.

License

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Author Information

#	Name	Institute / Affiliation
1	SIRITH N	VIDYA VIKAS INSTITUTE OF ENGINEERING AND TECHNOLOGY
2	SUBRAMANYA N S	VIDYA VIKAS INSTITUTE OF ENGINEERING AND TECHNOLOGY
3	SRINIVAS K R	VIDYA VIKAS INSTITUTE OF ENGINEERING AND TECHNOLOGY
4	NISARGA V	VIDYA VIKAS INSTITUTE OF ENGINEERING AND TECHNOLOGY
5	VIJAYANANDA	VIDYA VIKAS INSTITUTE OF ENGINEERING AND TECHNOLOGY

How to Cite

Use the following formats to cite this article in your research.

APA Style

N, SIRITH, S, SUBRAMANYA N, R, SRINIVAS K, V, NISARGA, & VIJAYANANDA (2022). SURFACE DEFECT DETECTION SYSTEM WITH MACHINE LEARNING. International Journal of Advance Research and Innovative Ideas In Education, 8(3), 5369-5374.

MLA Style

N, SIRITH, et al. "SURFACE DEFECT DETECTION SYSTEM WITH MACHINE LEARNING." International Journal of Advance Research and Innovative Ideas In Education, vol. 8, no. 3, 2022, pp. 5369-5374.

IEEE Style

SIRITH N, SUBRAMANYA N S, SRINIVAS K R, NISARGA V, and VIJAYANANDA, "SURFACE DEFECT DETECTION SYSTEM WITH MACHINE LEARNING," International Journal of Advance Research and Innovative Ideas In Education, vol. 8, no. 3, pp. 5369-5374, 2022.

Vancouver Style

N SIRITH, S SUBRAMANYA N, R SRINIVAS K, V NISARGA, VIJAYANANDA. SURFACE DEFECT DETECTION SYSTEM WITH MACHINE LEARNING. International Journal of Advance Research and Innovative Ideas In Education. 2022;8(3):5369-5374.

Harvard Style

N, SIRITH, S, SUBRAMANYA N, R, SRINIVAS K, V, NISARGA, & VIJAYANANDA (2022) 'SURFACE DEFECT DETECTION SYSTEM WITH MACHINE LEARNING', International Journal of Advance Research and Innovative Ideas In Education, 8(3), pp. 5369-5374.

Chicago Style

N, SIRITH, et al. "SURFACE DEFECT DETECTION SYSTEM WITH MACHINE LEARNING." International Journal of Advance Research and Innovative Ideas In Education 8, no. 3 (2022): 5369-5374.

Turabian Style

N, SIRITH, et al. "SURFACE DEFECT DETECTION SYSTEM WITH MACHINE LEARNING." International Journal of Advance Research and Innovative Ideas In Education 8, no. 3 (2022): 5369-5374.