Abstract: This system combines machine ... the combination of deep learning and traditional vision inspection system. The traditional vision inspection system includes image acquisition module, camera ...

Abstract: Many machine-learning-based defect detection methods ... So efficient detection in the end-edge-cloud architecture is a good solution to overcome the above challenges. A branchy deep ...

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 ...

Researchers have tested eight stand-alone deep learning ... gap in photovoltaic system research by integrating sophisticated defect detection techniques with machine learning ensemble methods ...

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 ...

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

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