# One-Shot Recognition of Manufacturing Defects in Steel Surfaces

arXiv: https://arxiv.org/abs/2005.05815 · HTML: https://mlsanity.com/paper/2005.05815/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2020.

## Built on by (most cited first, 13 of 13)

- [Vision HgNN: An Electron-Micrograph is Worth Hypergraph of Hypernodes](https://mlsanity.com/paper/2408.11351/) (2024)
- [Robust Anomaly Map Assisted Multiple Defect Detection with Supervised Classification Techniques](https://mlsanity.com/paper/2212.09352/) (2022)
- [RAFIC: Retrieval-Augmented Few-shot Image Classification](https://mlsanity.com/paper/2312.06868/) (2023)
- [Parameter-Efficient Quantized Mixture-of-Experts Meets Vision-Language Instruction Tuning for Semiconductor Electron Micrograph Analysis](https://mlsanity.com/paper/2408.15305/) (2024)
- [Intelligent Vision Based Wear Forecasting on Surfaces of Machine Tool Elements](https://mlsanity.com/paper/2106.06839/) (2021)
- [A tomographic workflow to enable deep learning for X-ray based foreign object detection](https://mlsanity.com/paper/2201.12184/) (2022)
- [Foundational Model for Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption](https://mlsanity.com/paper/2408.13248/) (2024)
- [Preliminary Investigations of a Multi-Faceted Robust and Synergistic Approach in Semiconductor Electron Micrograph Analysis: Integrating Vision Transformers with Large Language and Multimodal Models](https://mlsanity.com/paper/2408.13621/) (2024)
- [Hierarchical Network Fusion for Multi-Modal Electron Micrograph Representation Learning with Foundational Large Language Models](https://mlsanity.com/paper/2408.13661/) (2024)
- [EMCNet : Graph-Nets for Electron Micrographs Classification](https://mlsanity.com/paper/2409.03767/) (2024)
- [Sparks of Artificial General Intelligence(AGI) in Semiconductor Material Science: Early Explorations into the Next Frontier of Generative AI-Assisted Electron Micrograph Analysis](https://mlsanity.com/paper/2409.12244/) (2024)
- [One-Shot Identification with Different Neural Network Approaches](https://mlsanity.com/paper/2601.08278/) (2026)
- [Explainability for Fault Detection System in Chemical Processes](https://mlsanity.com/paper/2602.16341/) (2026)

## Abstract (arXiv)

Quality control is an essential process in manufacturing to make the product defect-free as well as to meet customer needs. The automation of this process is important to maintain high quality along with the high manufacturing throughput. With recent developments in deep learning and computer vision technologies, it has become possible to detect various features from the images with near-human accuracy. However, many of these approaches are data intensive. Training and deployment of such a system on manufacturing floors may become expensive and time-consuming. The need for large amounts of training data is one of the limitations of the applicability of these approaches in real-world manufacturing systems. In this work, we propose the application of a Siamese convolutional neural network to do one-shot recognition for such a task. Our results demonstrate how one-shot learning can be used in quality control of steel by identification of defects on the steel surface. This method can significantly reduce the requirements of training data and can also be run in real-time.