# Recognition Of Surface Defects On Steel Sheet Using Transfer Learning

arXiv: https://arxiv.org/abs/1909.03258 · HTML: https://mlsanity.com/paper/1909.03258/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2019.

## Built on by (most cited first, 7 of 7)

- [MemoryMamba: Memory-Augmented State Space Model for Defect Recognition](https://mlsanity.com/paper/2405.03673/) (2024)
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- [Structured Progressive Knowledge Activation for LLM-Driven Neural Architecture Search](https://mlsanity.com/paper/2605.04057/) (2026)
- [Overcoming the Weakest-Link Effect in LLM-Driven Program Optimization via Heterogeneous Edit Recombination](https://mlsanity.com/paper/2607.28947/) (2026)
- [GraphIR: Architecture-Level Search States for LLM-Guided Neural Architecture Evolution](https://mlsanity.com/paper/2608.01633/) (2026)
- [The Essence of Balance for Self-Improving Agents in Vision-and-Language Navigation](https://mlsanity.com/paper/2604.19064/) (2026)
- [An AI4AI Framework for Visual Token Pruning](https://mlsanity.com/paper/2608.07193/) (2026)

## Abstract (arXiv)

Automatic defect recognition is one of the research hotspots in steel production, but most of the current methods mainly extract features manually and use machine learning classifiers to recognize defects, which cannot tackle the situation, where there are few data available to train and confine to a certain scene. Therefore, in this paper, a new approach is proposed which consists of part of pretrained VGG16 as a feature extractor and a new CNN neural network as a classifier to recognize the defect of steel strip surface based on the feature maps created by the feature extractor. Our method achieves an accuracy of 99.1% and 96.0% while the dataset contains 150 images each class and 10 images each class respectively, which is much better than previous methods.