An AI system that learns manufacturing defect detection in seconds, achieving 99.9% accuracy without labeled data or ...

Source: US Army Field Manual FM 55-20 (public domain ... Collectively known in the railroad business as “defect detection,” the sensors and systems installed periodically along railroad ...

Dr. Muhammad Waqar Akram and his team from the Department of Farm Machinery and Power at University of Agriculture Faisalabad ...

“AI-based detection doesn’t just increase throughput,” said Alkoken. “It significantly reduces false alarms and simplifies defect binning. In production fabs, manual review workloads have decreased by ...

Traditional PV inspection methods using direct current polarisation have proved unreliable in detecting certain issues.

These benchmark methods include the Faster Region Based Convolutional Neural Network (FRCNN) with ResNet50, RetinaNet, and You-Only-Look-Once (YOLO) for defect detection and identification ...

Scientists from the federally funded Argonne National Laboratory in Illinois and the University of Virginia have developed a ...

The company plans to continue to enhance the AI by sharing relevant data through partnerships with substrate industry customers and suppliers, and expand the application to other image-based optical ...