Automatic Multiple Visual Target Acquisition And Tracking Using The APA512 Real Time Machine Vision Processor

1990 ◽  
Author(s):  
Thomas M. Seitzler
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Visual Target ◽  
Target Acquisition ◽  
Time Machine

scholarly journals A real-time machine-vision system for monitoring the textile raising process

2005 ◽  
Vol 56 (8-9) ◽  
pp. 831-842 ◽  
Author(s):  
Monica Carfagni ◽  
Rocco Furferi ◽  
Lapo Governi
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Vision System ◽  
Machine Vision System ◽  
Time Machine

Development of a real-time machine vision system for functional textile fabric defect detection using a deep YOLOv4 model

2021 ◽  
pp. 004051752110342
Author(s):  
Sifundvolesihle Dlamini ◽  
Chih-Yuan Kao ◽  
Shun-Lian Su ◽  
Chung-Feng Jeffrey Kuo
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Defect Detection ◽  
Data Augmentation ◽  
Detection System ◽  
Vision System ◽  
Good Precision ◽  
Machine Vision System ◽  
Time Machine ◽  
Textile Fabric

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.

FPGA-based multisensor real-time machine vision for banknote printing

2009 ◽  
Author(s):  
Rui Li ◽  
Thomas Türke ◽  
Johannes Schaede ◽  
Harald Willeke ◽  
Volker Lohweg
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Time Machine

Monitoring mineral wool production using real-time machine vision

1999 ◽  
Vol 5 (2) ◽  
pp. 125-140 ◽  
Author(s):  
F. Trdič ◽  
B. Širok ◽  
P.R. Bullen ◽  
D.R. Philpott
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Mineral Wool ◽  
Time Machine ◽  
Wool Production

Railway Fastener Inspection by Real-Time Machine Vision

2015 ◽  
Vol 45 (7) ◽  
pp. 1101-1107 ◽  
Author(s):  
Caglar Aytekin ◽  
Yousef Rezaeitabar ◽  
Sedat Dogru ◽  
Ilkay Ulusoy
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Time Machine

scholarly journals Real-Time Machine Vision System for an Automated Quality Monitoring in Mass Production of Multiaxial Non-Crimp Fabrics

2015 ◽  
Vol 48 (3) ◽  
pp. 2393-2398 ◽  
Author(s):  
R. Schmitt ◽  
T. Fürtjes ◽  
B. Abbas ◽  
P. Abel ◽  
W. Kimmelmann ◽  
...  
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Mass Production ◽  
Vision System ◽  
Quality Monitoring ◽  
Machine Vision System ◽  
Time Machine

Relative Threshold-Based Positioning Adaptive Algorithm for Real-Time Machine Vision

2011 ◽  
Vol 127 ◽  
pp. 368-373
Author(s):  
Yu Bin Xia ◽  
Zhong Wei Guo
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Time Machine ◽  
Uniform Illumination ◽  
Step Method ◽  
Relative Threshold ◽  
Vision Positioning ◽  
Object Center ◽  
Positioning Algorithm ◽  
Adaptive Step

For the traditional machine vision, positioning algorithms are usually less efficient and more complex, the author proposes a relative threshold-based positioning algorithm for real-time machine vision. Firstly, the algorithm thresholds the template and sample images with a relative threshold. So it can not only effectively impact the influence of uniform illumination, but also reduce the volume of data. Then it uses the two-floor image pyramid method to greatly reduce the computation amount and uses the adaptive step method further to accelerate the matching speed. The algorithm nears to the object by the rough matching, and then navigates to the object center through a precise matching. While greatly improving the matching speed it ensures the accuracy. The experiments show that it can meet the real-time requirement.

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