# TLU-Net: A Deep Learning Approach for Automatic Steel Surface Defect Detection

arXiv: https://arxiv.org/abs/2101.06915 · HTML: https://mlsanity.com/paper/2101.06915/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 18 Jan 2021.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Segmentation-Based Deep-Learning Approach for Surface-Defect Detection](https://mlsanity.com/paper/1903.08536/) (2019)

## Built on by (most cited first, 6 of 6)

- [LSTM-CNN Network for Audio Signature Analysis in Noisy Environments](https://mlsanity.com/paper/2312.07059/) (2023)
- [Decision Fusion Network with Perception Fine-tuning for Defect Classification](https://mlsanity.com/paper/2309.12630/) (2023)
- [Improving the classification of extreme classes by means of loss regularisation and generalised beta distributions](https://mlsanity.com/paper/2407.12417/) (2024)
- [Enhancing Microgrid Performance Prediction with Attention-based Deep Learning Models](https://mlsanity.com/paper/2407.14984/) (2024)
- [ScratNet: A Swin-Based Multi-Scale Dilated Network with Precision Refinement for Semiconductor Scratch Segmentation](https://mlsanity.com/paper/2607.10214/) (2026)
- [RobustDefect-LLM: Explainable and Robustness-Aware Industrial Surface Defect Classification with Decision Support and AI-Assisted Reporting](https://mlsanity.com/paper/2608.08589/) (2026)

## Abstract (arXiv)

Visual steel surface defect detection is an essential step in steel sheet manufacturing. Several machine learning-based automated visual inspection (AVI) methods have been studied in recent years. However, most steel manufacturing industries still use manual visual inspection due to training time and inaccuracies involved with AVI methods. Automatic steel defect detection methods could be useful in less expensive and faster quality control and feedback. But preparing the annotated training data for segmentation and classification could be a costly process. In this work, we propose to use the Transfer Learning-based U-Net (TLU-Net) framework for steel surface defect detection. We use a U-Net architecture as the base and explore two kinds of encoders: ResNet and DenseNet. We compare these nets' performance using random initialization and the pre-trained networks trained using the ImageNet data set. The experiments are performed using Severstal data. The results demonstrate that the transfer learning performs 5% (absolute) better than that of the random initialization in defect classification. We found that the transfer learning performs 26% (relative) better than that of the random initialization in defect segmentation. We also found the gain of transfer learning increases as the training data decreases, and the convergence rate with transfer learning is better than that of the random initialization.