Abstract: With the rapid development of deep learning, automatic defect detection has been introduced into various manufacturing pipelines. Many studies on defect inspection focus on training an ...

Developed a deep learning-based framework for automated defect detection and pattern recognition in semiconductor wafer images. Utilized AutoEncoder Convolutional Neural Networks (CNNs) with PyTorch ...

In this work, we train a deep learning architecture EfficientDet to automatically detect defects from ultrasonic images. We showed how some of the hyperparameters can be tweaked in order to improve ...

This research investigates deep learning-based approach for defect detection in the steel production using Severstal steel dataset. The developed system integrates DenseNet121 for classification and ...

“We report a complete deep-learning framework using a single-step object detection model in order to quickly and accurately detect and classify the types of manufacturing defects present on Printed ...

Researchers have tested eight stand-alone deep learning methods for PV cell fault detection and have ... photovoltaic panel cell defects classification using deep learning ensemble methods ...