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.

Date Created 2020-09-06 (4 years ago)

Commits 14 (last one 3 years ago)

Stargazers 52 (0 this week)

Watchers 1 (0 this week)

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RepositoryStats indexes 631,351 repositories, of these aviralchharia/Surface-Defect-Detection-in-Hot-Rolled-Steel-Strips is ranked #477,416 (24th percentile) for total stargazers, and #556,009 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #12,717/19,010.

aviralchharia/Surface-Defect-Detection-in-Hot-Rolled-Steel-Strips is also tagged with popular topics, for these it's ranked: deep-learning (#7,122/8800), artificial-intelligence (#1,800/2274), neural-networks (#633/758), cnn (#455/572), classification (#456/572), detection (#407/468)

All Topics

cnn detection deep-learning manufacturing classification neural-networks defect-detection mechanical-engineering artificial-intelligence convolutional-neural-network

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updated: 2025-03-15 @ 02:22pm, id: 293352172 / R_kgDOEXwy7A