The processed data is combined with deep learning and based on neural network model ResNet. Meanwhile the processed image data is sent to a convolutional neural network (CNN) for model training. We ...

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

This project explores the use of deep learning techniques to create an automated system for detecting defects in insulators used ... The approach combines YOLO-based object detection with enhancements ...

Developed a deep learning-based framework for automated defect detection and pattern recognition in semiconductor ... Implemented an AutoEncoder CNN architecture, which was instrumental in learning ...

This new technical paper titled “End-to-end deep learning ... detection model in order to quickly and accurately detect and classify the types of manufacturing defects present on Printed Circuit Board ...

Machine-learning systems are used to identify objects ... discover the representations needed for detection or classification. Deep-learning methods are representation-learning methods with ...

Researchers from Northwestern University, University of Virginia, Carnegie Mellon University, and Argonne National Laboratory have made a significant advancement in defect detection and ...

The processed data is combined with deep learning and based on neural network model ResNet. Meanwhile the processed image data is sent to a convolutional neural network (CNN) for model training. We ...