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

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 ...

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 ...

This research presents a deep learning-based automated product defect detection system to address limitations of conventional manual inspection techniques that are labor-intensive and prone to errors.

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 ...

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 ...

Abstract: In the bearing manufacturing industry, detecting defects in bearings is essential for maintaining product quality and operational efficiency. Traditional ...