# Enhancing Glass Defect Detection with Diffusion Models: Addressing Imbalanced Datasets in Manufacturing Quality Control

arXiv: https://arxiv.org/abs/2505.03134 · HTML: https://mlsanity.com/paper/2505.03134/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 17 of its 290 references that are in the map.
- First posted on arXiv 6 May 2025.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

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

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [Diffusion Models for Medical Anomaly Detection](https://mlsanity.com/paper/2203.04306/) (2022)
- [Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data](https://mlsanity.com/paper/1508.03422/) (2015)
- [Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning](https://mlsanity.com/paper/1808.02518/) (2018)
- [Prioritizing App Reviews for Developer Responses on Google Play](https://mlsanity.com/paper/2502.01520/) (2025)
- [Network Anomaly Detection for IoT Using Hyperdimensional Computing on NSL-KDD](https://mlsanity.com/paper/2503.03031/) (2025)
- [Addressing Small and Imbalanced Medical Image Datasets Using Generative Models: A Comparative Study of DDPM and PGGANs with Random and Greedy K Sampling](https://mlsanity.com/paper/2412.12532/) (2024)
- [Machine Learning Models for Reinforced Concrete Pipes Condition Prediction: The State-of-the-Art Using Artificial Neural Networks and Multiple Linear Regression in a Wisconsin Case Study](https://mlsanity.com/paper/2502.00363/) (2025)
- [rPPG-SysDiaGAN: Systolic-Diastolic Feature Localization in rPPG Using Generative Adversarial Network with Multi-Domain Discriminator](https://mlsanity.com/paper/2504.01220/) (2025)

## Built on by (most cited first, 3 of 3)

- [Sanitizing Manufacturing Dataset Labels Using Vision-Language Models](https://mlsanity.com/paper/2506.23465/) (2025)
- [Quantifying Label-Induced Bias in Large Language Model Self- and Cross-Evaluations](https://mlsanity.com/paper/2508.21164/) (2025)
- [Spatial Attention Supervision for Defect Localization: Exploiting Ground-Truth Masks as Training Signal in Diffusion-Augmented Defect Detection](https://mlsanity.com/paper/2609.06232/) (2026)

## Abstract (arXiv)

Visual defect detection in industrial glass manufacturing remains a critical challenge due to the low frequency of defective products, leading to imbalanced datasets that limit the performance of deep learning models and computer vision systems. This paper presents a novel approach using Denoising Diffusion Probabilistic Models (DDPMs) to generate synthetic defective glass product images for data augmentation, effectively addressing class imbalance issues in manufacturing quality control and automated visual inspection. The methodology significantly enhances image classification performance of standard CNN architectures (ResNet50V2, EfficientNetB0, and MobileNetV2) in detecting anomalies by increasing the minority class representation. Experimental results demonstrate substantial improvements in key machine learning metrics, particularly in recall for defective samples across all tested deep neural network architectures while maintaining perfect precision on the validation set. The most dramatic improvement was observed in ResNet50V2's overall classification accuracy, which increased from 78% to 93% when trained with the augmented data. This work provides a scalable, cost-effective approach to enhancing automated defect detection in glass manufacturing that can potentially be extended to other industrial quality assurance systems and industries with similar class imbalance challenges.