aviralchharia/Surface-Defect-Detection-in-Hot-Rolled-Steel-Strips

This project aims to automatically detect surface defects in Hot-Rolled Steel Strips such as rolled-in scale, patches, crazing, pitted surface, inclusion and scratches. A CNN is trained on the NEU Metal Surface Defects Database which contains 1800 grayscale images with 300 samples of each of the six different kinds of surface defects.

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This project helps quality control engineers and manufacturing line operators automatically identify common surface flaws like rolled-in scale or scratches on hot-rolled steel strips. By inputting grayscale images of steel surfaces, it outputs a classification of any detected defect. This tool is designed for professionals in steel manufacturing who need to quickly and accurately inspect product quality.

No commits in the last 6 months.

Use this if you need an automated way to detect and classify surface defects on hot-rolled steel strips using image data.

Not ideal if you are working with other material types or require detection of internal defects not visible on the surface.

steel-manufacturing quality-control defect-detection materials-inspection industrial-automation
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 18 / 25

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Language

Jupyter Notebook

License

MIT

Last pushed

Jun 15, 2021

Commits (30d)

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