Optical sensor for real-time weld defect detection

Author(s):  
Antonio Ancona ◽  
Tommaso Maggipinto ◽  
Vincenzo Spagnolo ◽  
Michele Ferrara ◽  
Pietro M. Lugara
2005 ◽  
Vol 1 (4) ◽  
pp. 259-273 ◽  
Author(s):  
D. Naso ◽  
B. Turchiano ◽  
P. Pantaleo

2000 ◽  
Author(s):  
Antonio Baldassarre ◽  
Maurizio De Lucia ◽  
Francesca Rossi ◽  
Massimiliano Vannucci

2021 ◽  
pp. 004051752110342
Author(s):  
Sifundvolesihle Dlamini ◽  
Chih-Yuan Kao ◽  
Shun-Lian Su ◽  
Chung-Feng Jeffrey Kuo

We introduce a real-time machine vision system we developed with the aim of detecting defects in functional textile fabrics with good precision at relatively fast detection speeds to assist in textile industry quality control. The system consists of image acquisition hardware and image processing software. The software we developed uses data preprocessing techniques to break down raw images to smaller suitable sizes. Filtering is employed to denoise and enhance some features. To generalize and multiply the data to create robustness, we use data augmentation, which is followed by labeling where the defects in the images are labeled and tagged. Lastly, we utilize YOLOv4 for localization where the system is trained with weights of a pretrained model. Our software is deployed with the hardware that we designed to implement the detection system. The designed system shows strong performance in defect detection with precision of [Formula: see text], and recall and [Formula: see text] scores of [Formula: see text] and [Formula: see text], respectively. The detection speed is relatively fast at [Formula: see text] fps with a prediction speed of [Formula: see text] ms. Our system can automatically locate functional textile fabric defects with high confidence in real time.


2017 ◽  
Vol 17 ◽  
pp. 135-142 ◽  
Author(s):  
Oliver Holzmond ◽  
Xiaodong Li

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