To improve the precision of defect categorization and localization in images, this paper proposes an approach for detecting surface defects in hot-rolled steel strips. The approach uses an improved ...

Graphene grids exhibit exceptional loading capacity for macromolecules, single atoms, and nanoparticles, offering significant potential for exploring the structure and properties of various materials ...

Researchers have designed a robust image-based anomaly detection (AD) framework with illumination enhancement and noise suppression features that can enhance the detection of subtle defects in ...

“Semiconductor lithography inspection requires reliable detection of small pattern defects such as bridge, burr, pinch, and contamination. In this study, we propose a two-stage vision-language ...

In biology, defects are generally bad. But in materials science, defects can be intentionally tuned to give materials useful new properties. Today, atomic-scale defects are carefully introduced during ...

The novel method uses the YOLOv8 framework, integrating an attention mechanism and a transformer model. It was tested on a dataset of 4,500 electroluminescence images against several other models and ...

Two alumni of Greenwood High International School have secured first place in the poster competition at Purdue University's ...

AI plays a role in improving defect capture rate and distinguishing between yield-killing and nuisance defects. New developments in wafer edge inspection are proving essential to bonded wafer yields.

Machine vision helps poultry processors automate efficiently. Explore how AI-based vision systems identify defects, prevent costly mistakes, and guide automation strategy.

This lightweight, portable tool improves NDT inspections with multiple test capabilities, high-fidelity imaging, next-gen ...