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

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.

At present, surface defect equipment based on machine vision has widely replaced artificial visual inspection in various industrial fields, including 3C, automobiles, home appliances, machinery ...

Abstract: Automatic defect detection on the steel surface is a challenging task in computer vision, owing to miscellaneous patterns of the defects, low contrast between the defect and background, the ...

Manual quality control methods limit the efficiency and effectiveness of modern manufacturing processes. For one, manual inspection is too inconsistent. Due to factors like fatigue, human inspectors ...

This repository is related to our blog post Detect industrial defects at low latency with computer vision at the edge with Amazon SageMaker Edge in the AWS Machine Learning blog. In this workshop, we ...

Abstract: We present our automated real-time socket inspection system capable of detecting an assortment of defects including metallic and liquid staining, loose capacitors and pins, and other debris ...

A study by the Waste and Resources Action Programme found that up to 15% of knitted fabric goes to waste during production, due to defects such as snags and needle lines. This can result in ...