A new study explores deep learning for image-based defect detection during 3D printing, looking to catch bad builds.

“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 ...

A new research review looks at how computer vision and machine learning could be used to spot defects in 3D printed concrete. That sounds like a narrow research topic. It isn’t. Construction 3D ...

Machine vision for defect detection and recognition has evolved from classical image‐processing workflows—such as thresholding, edge detection and template matching—to sophisticated deep learning ...

A high-precision, real-time system to detect defects in fabric (Hole, Oil, Crack, Stain, Damage) using YOLOv8. This project features a modern Flask-based web interface for easy interaction and ...

The system, developed by Panevo, a Canadian clear technology and manufacturing analytics company, reportedly achieved approximately 97% detection reliability with minimal false positives of Muskoka’s ...

ABSTRACT: Rail defects, both internal and external, pose significant safety risks. Acoustic Emission (AE) technology has emerged as a promising method for monitoring damage progression and detecting ...

Wood, a widely distributed renewable resource, plays a vital role in accelerating urbanisation. However, wood grain defects pose significant safety hazards. Detecting these defects is challenging due ...

To address the issues of missed detection and false detection during the defect inspection process of the PCB, an improved YOLOv7-based algorithm for PCB defect detection is proposed. Firstly, the ...