  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.
