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.
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.
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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.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Jun 15, 2021
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