# An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products

arXiv: https://arxiv.org/abs/2107.11643 · HTML: https://mlsanity.com/paper/2107.11643/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 24 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://mlsanity.com/paper/1801.00553/) (2018)
- [An Uncertainty-aware Transfer Learning-based Framework for Covid-19 Diagnosis](https://mlsanity.com/paper/2007.14846/) (2020)
- [Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection](https://mlsanity.com/paper/2012.11840/) (2020)
- [Scalable Gaussian Process Classification via Expectation Propagation](https://mlsanity.com/paper/1507.04513/) (2015)
- [Automatic Polyp Segmentation using U-Net-ResNet50](https://mlsanity.com/paper/2012.15247/) (2020)
- [Confidence Aware Neural Networks for Skin Cancer Detection](https://mlsanity.com/paper/2107.09118/) (2021)
- [Locally Differentially Private Naive Bayes Classification](https://mlsanity.com/paper/1905.01039/) (2019)

## Built on by (most cited first, 4 of 4)

- [Noise-Augmented Boruta: The Neural Network Perturbation Infusion with Boruta Feature Selection](https://mlsanity.com/paper/2309.09694/) (2023)
- [Beyond Uncertainty Quantification: Learning Uncertainty for Trust-Informed Neural Network Decisions - A Case Study in COVID-19 Classification](https://mlsanity.com/paper/2410.02805/) (2024)
- [Proximity-Based Evidence Retrieval for Uncertainty-Aware Neural Networks](https://mlsanity.com/paper/2509.13338/) (2025)
- [Trustworthy Visual Quality Inspection under Data Scarcity in Manufacturing](https://mlsanity.com/paper/2608.21967/) (2026)

## Abstract (arXiv)

Defects are unavoidable in casting production owing to the complexity of the casting process. While conventional human-visual inspection of casting products is slow and unproductive in mass productions, an automatic and reliable defect detection not just enhances the quality control process but positively improves productivity. However, casting defect detection is a challenging task due to diversity and variation in defects' appearance. Convolutional neural networks (CNNs) have been widely applied in both image classification and defect detection tasks. Howbeit, CNNs with frequentist inference require a massive amount of data to train on and still fall short in reporting beneficial estimates of their predictive uncertainty. Accordingly, leveraging the transfer learning paradigm, we first apply four powerful CNN-based models (VGG16, ResNet50, DenseNet121, and InceptionResNetV2) on a small dataset to extract meaningful features. Extracted features are then processed by various machine learning algorithms to perform the classification task. Simulation results demonstrate that linear support vector machine (SVM) and multi-layer perceptron (MLP) show the finest performance in defect detection of casting images. Secondly, to achieve a reliable classification and to measure epistemic uncertainty, we employ an uncertainty quantification (UQ) technique (ensemble of MLP models) using features extracted from four pre-trained CNNs. UQ confusion matrix and uncertainty accuracy metric are also utilized to evaluate the predictive uncertainty estimates. Comprehensive comparisons reveal that UQ method based on VGG16 outperforms others to fetch uncertainty. We believe an uncertainty-aware automatic defect detection solution will reinforce casting productions quality assurance.