Deep-learning-based machine-vision system for defect detection of fiber interlock cable

2021 ◽  
Author(s):  
Zhaowei Chen ◽  
Mikey R. Holtz ◽  
Samuel J. Vidourek ◽  
Hossein Alisafaee
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Defect Detection ◽  
Vision System ◽  
Machine Vision System

A novel method based on machine vision system and deep learning to detect fraud in turmeric powder

2021 ◽  
pp. 104728
Author(s):  
Ahmad Jahanbakhshi ◽  
Yousef Abbaspour-Gilandeh ◽  
Kobra Heidarbeigi ◽  
Mohammad Momeny
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Vision System ◽  
Machine Vision System ◽  
Novel Method

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.

Machine Vision System for Automatic Inspection of Surface Defects in Aluminum Die Casting

2006 ◽  
Vol 10 (3) ◽  
pp. 281-286 ◽  
Author(s):  
Yakov Frayman ◽  
◽  
Hong Zheng ◽  
Saeid Nahavandi ◽  
Keyword(s):  
Genetic Algorithm ◽  
Machine Vision ◽  
Defect Detection ◽  
Surface Defects ◽  
Vision System ◽  
Die Casting ◽  
Noise Removal ◽  
Morphological Operations ◽  
Automatic Inspection ◽  
Machine Vision System

A camera based machine vision system for the automatic inspection of surface defects in aluminum die casting is presented. The system uses a hybrid image processing algorithm based on mathematic morphology to detect defects with different sizes and shapes. The defect inspection algorithm consists of two parts. One is a parameter learning algorithm, in which a genetic algorithm is used to extract optimal structuring element parameters, and segmentation and noise removal thresholds. The second part is a defect detection algorithm, in which the parameters obtained by a genetic algorithm are used for morphological operations. The machine vision system has been applied in an industrial setting to detect two types of casting defects: parts mix-up and any defects on the surface of castings. The system performs with a 99% or higher accuracy for both part mix-up and defect detection and is currently used in industry as part of normal production.

scholarly journals Implementation and potentials of a machine vision system in a series production using deep learning and low-cost hardware

Procedia CIRP ◽  
2020 ◽  
Vol 90 ◽  
pp. 611-616
Author(s):  
Hubert Würschinger ◽  
Matthias Mühlbauer ◽  
Michael Winter ◽  
Michael Engelbrecht ◽  
Nico Hanenkamp
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Vision System ◽  
Low Cost ◽  
Machine Vision System ◽  
Series Production

Surface Defect Detection Using SVM‐Based Machine Vision System with Optimized Feature

2021 ◽  
pp. 109-127
Author(s):  
Ashok Kumar Patel ◽  
Venkata Naresh Mandhala ◽  
Dinesh Kumar Anguraj ◽  
Soumya Ranjan Nayak
Keyword(s):  
Machine Vision ◽  
Defect Detection ◽  
Surface Defect ◽  
Vision System ◽  
Machine Vision System ◽  
Surface Defect Detection
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