# A Comprehensive Survey on Machine Learning Driven Material Defect Detection

arXiv: https://arxiv.org/abs/2406.07880 · HTML: https://mlsanity.com/paper/2406.07880/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 209 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 17 of 17)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions](https://mlsanity.com/paper/2006.02903/) (2020)
- [Segmentation-Based Deep-Learning Approach for Surface-Defect Detection](https://mlsanity.com/paper/1903.08536/) (2019)
- [Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection](https://mlsanity.com/paper/2103.15158/) (2021)
- [G2D: Generate to Detect Anomaly](https://mlsanity.com/paper/2006.11629/) (2020)
- [Machine learning and deep learning](https://mlsanity.com/paper/2104.05314/) (2021)
- [Online PCB Defect Detector On A New PCB Defect Dataset](https://mlsanity.com/paper/1902.06197/) (2019)
- [Anomaly Detection via Self-organizing Map](https://mlsanity.com/paper/2107.09903/) (2021)
- [Microstructure Representation and Reconstruction of Heterogeneous Materials via Deep Belief Network for Computational Material Design](https://mlsanity.com/paper/1612.07401/) (2016)
- [Unsupervised machine learning via transfer learning and k-means clustering to classify materials image data](https://mlsanity.com/paper/2007.08361/) (2020)
- [Attention mechanisms and deep learning for machine vision: A survey of the state of the art](https://mlsanity.com/paper/2106.07550/) (2021)
- [Defect Transformer: An Efficient Hybrid Transformer Architecture for Surface Defect Detection](https://mlsanity.com/paper/2207.08319/) (2022)
- [DeepInspect: An AI-Powered Defect Detection for Manufacturing Industries](https://mlsanity.com/paper/2311.03725/) (2023)
- [Print Defect Mapping with Semantic Segmentation](https://mlsanity.com/paper/2001.10111/) (2020)
- [A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data](https://mlsanity.com/paper/2203.05733/) (2022)
- [Reinforcement Learning-based Defect Mitigation for Quality Assurance of Additive Manufacturing](https://mlsanity.com/paper/2210.17272/) (2022)
- [3-Dimensional residual neural architecture search for ultrasonic defect detection](https://mlsanity.com/paper/2311.01867/) (2023)

## Built on by (most cited first, 5 of 5)

- [Legal Document Summarization: Enhancing Judicial Efficiency through Automation Detection](https://mlsanity.com/paper/2507.18952/) (2025)
- [Automated Annotation of Shearographic Measurements Enabling Weakly Supervised Defect Detection](https://mlsanity.com/paper/2512.06171/) (2025)
- [PoiCGAN: A Targeted Poisoning Based on Feature-Label Joint Perturbation in Federated Learning](https://mlsanity.com/paper/2603.23574/) (2026)
- [Data-Driven Estimation of the interfacial Dzyaloshinskii-Moriya Interaction with Machine Learning](https://mlsanity.com/paper/2603.28812/) (2026)
- [SynSur: An end-to-end generative pipeline for synthetic industrial surface defect generation and detection](https://mlsanity.com/paper/2604.26633/) (2026)

## Abstract (arXiv)

Material defects (MD) represent a primary challenge affecting product performance and giving rise to safety issues in related products. The rapid and accurate identification and localization of MD constitute crucial research endeavors in addressing contemporary challenges associated with MD. In recent years, propelled by the swift advancement of machine learning (ML) technologies, particularly exemplified by deep learning, ML has swiftly emerged as the core technology and a prominent research direction for material defect detection (MDD). Through a comprehensive review of the latest literature, we systematically survey the ML techniques applied in MDD into five categories: unsupervised learning, supervised learning, semi-supervised learning, reinforcement learning, and generative learning. We provide a detailed analysis of the main principles and techniques used, together with the advantages and potential challenges associated with these techniques. Furthermore, the survey focuses on the techniques for defect detection in composite materials, which are important types of materials enjoying increasingly wide application in various industries such as aerospace, automotive, construction, and renewable energy. Finally, the survey explores potential future directions in MDD utilizing ML technologies. This survey consolidates ML-based MDD literature and provides a foundation for future research and practice.