This paper presents an automated machine vision system for the quality inspection of blanked metal components by the analysis of product images. The binary classification to identify defective samples among the compliant ones is carried out by means of two different supervised machine learning algorithms, a Sequential Neural Network (SNN) and a Support Vector Machine (SVM). The features used by the algorithms are extracted from the images using a novel approach, that exploits some geometrical characteristics of the product. Indeed, the spatial features of the target object in the images are processed as they represent a time series signal and hence features are extracted and exploited as input data for the classification task.
Automated Vision Inspection of Critical Steel Components based on Signal Analysis Extracted form Images
Brambilla P.;Chiariotti P.;Tarabini M.
2022-01-01
Abstract
This paper presents an automated machine vision system for the quality inspection of blanked metal components by the analysis of product images. The binary classification to identify defective samples among the compliant ones is carried out by means of two different supervised machine learning algorithms, a Sequential Neural Network (SNN) and a Support Vector Machine (SVM). The features used by the algorithms are extracted from the images using a novel approach, that exploits some geometrical characteristics of the product. Indeed, the spatial features of the target object in the images are processed as they represent a time series signal and hence features are extracted and exploited as input data for the classification task.File | Dimensione | Formato | |
---|---|---|---|
Automated_Vision_Inspection_of_Critical_Steel_Components_based_on_Signal_Analysis_Extracted_form_Images.pdf
Accesso riservato
:
Publisher’s version
Dimensione
1.95 MB
Formato
Adobe PDF
|
1.95 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.