# Steel surface defect detection and segmentation using deep neural networks

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/steel-surface-defect-detection-and-segmentation-using-deep-neural-networks/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Sara Ashrafi,Sobhan Teymouri,Sepideh Etaati,Javad Khoramdel,Yasamin Borhani,Esmaeil Najafi |
| Citations | 40 |
| DOI | 10.1016/j.rineng.2025.103972 |
| Fields | Computer Science,Engineering |
| Open Access | true |
| OA Status | gold |
| OA URL | https://doi.org/10.1016/j.rineng.2025.103972 |
| OpenAlex ID | https://openalex.org/W4406199446 |
| Type | article |
| Year | 2025 |

## Paper authors

- [Sobhan Teymouri](https://scholariq.org/researchers/sobhan-teymouri/)

## Paper journal

- [Results in Engineering](https://scholariq.org/journals/results-in-engineering/)

## Paper primary topic

- [Industrial Vision Systems and Defect Detection](https://scholariq.org/topics/industrial-vision-systems-and-defect-detection/)

## Paper topics

- [Industrial Vision Systems and Defect Detection](https://scholariq.org/topics/industrial-vision-systems-and-defect-detection/)
- [Surface Roughness and Optical Measurements](https://scholariq.org/topics/surface-roughness-and-optical-measurements/)
- [Advanced Neural Network Applications](https://scholariq.org/topics/advanced-neural-network-applications/)

---
Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.