scholarly journals Vision-Based Surface Inspection System for Bearing Rollers Using Convolutional Neural Networks

2018 ◽  
Vol 8 (12) ◽  
pp. 2565 ◽  
Author(s):  
Shengping Wen ◽  
Zhihong Chen ◽  
Chaoxian Li
Keyword(s):  
Neural Network ◽  
Machine Vision ◽  
Convolutional Neural Network ◽  
Vision System ◽  
Inspection System ◽  
Mechanical Transmission ◽  
Surface Inspection ◽  
Machine Vision System ◽  
Factors Affecting ◽  
Bearing Rollers

Bearings are commonly used machine elements and an important part of mechanical transmission. They are widely used in automobiles, airplanes, and various instruments and equipment. Bearing rollers are the most important components in a bearing and determine the performance, life, and stability of the bearing. In order to control the surface quality of the rollers, a machine vision system for bearing roller surface inspection is proposed. We briefly introduced the design of the machine vision system and then focused on the surface inspection algorithm. We proposed a multi-task convolutional neural network to detect defects. We extracted the features of the defects through a shared convolutional neural network, then classified the defects and calculated the position of the defects simultaneously. Finally, we determined if the bearing roller was qualified according to the position, category, and area of the defect. In addition, we explored various factors affecting performance and conducted a large number of experiments. We compared our method with the traditional methods and proved that our method had good stability and robustness.

UAV Navigation System Autonomous Correction Algorithm Based on Road and River Network Recognition

2020 ◽  
Vol 28 (3) ◽  
pp. 32-42
Author(s):  
A.P. Tanchenko ◽  
◽  
A.M. Fedulin ◽  
R.R. Bikmaev ◽  
R.N. Sadekov ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Vision ◽  
Convolutional Neural Network ◽  
Navigation System ◽  
Vision System ◽  
River Network ◽  
Machine Vision System ◽  
Topographic Map ◽  
Correction Algorithm ◽  
Uav Navigation

The paper considers an original autonomous correction algorithm for UAV navigation system based on comparison between terrain images obtained by onboard machine vision system and vector topographic map images. Comparison is performed by calculating the homography of vision system images segmented using the convolutional neural network and the vector map images. The presented results of mathematical and flight experiments confirm the algorithm effectiveness for navigation applications.

scholarly journals Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization

2020 ◽  
Author(s):  
Amit Kumar Gorai ◽  
Simit Raval ◽  
Ashok Kumar Patel ◽  
Snehamoy Chatterjee ◽  
Tarini Gautam
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Machine Vision ◽  
Vision System ◽  
Color Space ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Machine Vision System ◽  
Coal Characterization ◽  
Artificial Neural

Abstract Coal is heterogeneous in nature, and thus the characterization of coal is essential before its use for a specific purpose. Thus, the current study aims to develop a machine vision system for automated coal characterizations. The model was calibrated using 80 image samples that are captured for different coal samples in different angles. All the images were captured in RGB color space and converted into five other color spaces (HSI, CMYK, Lab, xyz, Gray) for feature extraction. The intensity component image of HSI color space was further transformed into four frequency components (discrete cosine transform, discrete wavelet transform, discrete Fourier transform, and Gabor filter) for the texture features extraction. A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development. The datasets of the optimized features were used as an input for the model, and their respective coal characteristics (analyzed in the laboratory) were used as outputs of the model. The R-squared values were found to be 0.89, 0.92, 0.92, and 0.84, respectively, for fixed carbon, ash content, volatile matter, and moisture content. The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression, support vector regression, and radial basis neural network models. The study demonstrates the potential of the machine vision system in automated coal characterization.

Automatic Inspection System of Small Plastic Gears Based on Machine Vision

2012 ◽  
Vol 522 ◽  
pp. 628-633 ◽  
Author(s):  
Jian Zhe Chen ◽  
Gui Tang Wang ◽  
Jian Qiang Chen ◽  
Xin Liang Yin
Keyword(s):  
Machine Vision ◽  
Template Matching ◽  
Vision System ◽  
Inspection System ◽  
Injection Molding Process ◽  
Automatic Inspection ◽  
Machine Vision System ◽  
Molding Process ◽  
Small Plastic ◽  
Plastic Gears

Small plastic gear is generally made by injection molding.But the injection molding process and mold have problems with missing tooth, shrink, more material, less material and inaccurate roundness and so on. Furthermore, using manual inspection will appear phenomenon of low efficiency, false detection and leak detection. To solve these problems, this paper introduces an automatic inspection system of small plastic gears based on machine vision. The system consists of feeding and sorting machine control system and machine vision inspection system of the gear defects. Mechanical control use digital servo control technology to achieve automatic nesting, feeding, positioning of gear workpiece, and depend on the inspection result of machine vision system to sort. After acquired gear image through a camera, Machine vision system uses median filtering, binarization, edge detection algorithms to process image. Then the system adopts template matching algorithm to obtain the inspection result and send the result to the sorting controller, which achieve automatic smart inspection of gear. The automatic inspection system has accurate, efficient, intelligent and other advantages.

Identification of Peanut Pods with Three or More Kernels by Machine Vision and Neural Network

2014 ◽  
Vol 10 (1) ◽  
pp. 97-102
Author(s):  
Jason Wang ◽  
Wade W. Yang ◽  
Lloyd T. Walker ◽  
Taha Rababah
Keyword(s):  
Neural Network ◽  
Machine Vision ◽  
Vision System ◽  
Major Axis ◽  
Machine Vision System ◽  
Discrimination Accuracy ◽  
Projected Area ◽  
Niche Marketing ◽  
Axis Length ◽  
Physical Features

Abstract Separation of unshelled peanuts containing three or more kernels and then niche marketing them can potentially increase the value of unshelled peanuts and thus the profit of peanut producers or processors. Effective identification of peanut pods with three or more kernels is a critical step prior to separation. In this study, a machine vision system was teamed up with neural network technique to discriminate unshelled peanuts into two groups: one with three or more kernels and the other with two or less kernels. A set of physical features including the number of bumps, projected area, length and perimeter, etc., were extracted from the images taken and used to train an artificial neural network for discriminating the peanuts. It was found that among all the selected features, the length, the major axis length and perimeter have the best correlation with the number of kernels (correlation coefficient r = 0.87–0.88); the area and convex area have good correlation (r = 0.85); the eccentricity, number of bumps, and the compactness have relatively lower correction (r = 0.77–0.80); the solidity and the minor axis length have the least correlation to the number of kernels (r = −0.415–0.26). The best discrimination accuracy obtained for peanut pods with three or more kernels was 92.5% for the conditions used in this study.

Machine vision system for curved surface inspection

2000 ◽  
Vol 12 (4) ◽  
pp. 177-188 ◽  
Author(s):  
Min-Fan Ricky Lee ◽  
Clarence W. de Silva ◽  
Elizabeth A. Croft ◽  
Q.M. Jonathan Wu
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Curved Surface ◽  
Surface Inspection ◽  
Machine Vision System

Unmanned machine vision system for automated recognition of mechanical parts

2018 ◽  
Vol 6 (4) ◽  
pp. 184-196
Author(s):  
Tushar Jain ◽  
Meenu Gupta ◽  
H.K. Sardana
Keyword(s):  
Neural Network ◽  
Machine Vision ◽  
Classification Accuracy ◽  
Network Architecture ◽  
Vision System ◽  
Confusion Matrix ◽  
Machine Vision System ◽  
Content Type ◽  
Recognition Of Objects ◽  
Hidden Nodes

Purpose The field of machine vision, or computer vision, has been growing at fast pace. The growth in this field, unlike most established fields, has been both in breadth and depth of concepts and techniques. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. The goal of a machine vision system is to create a model of the real world from images. Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. The purpose of this paper is to consider recognition of objects manufactured in mechanical industry. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such parts. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects. Design/methodology/approach The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts. Findings Classification accuracy is affected by the changing network architecture. ANN is computationally demanding and slow. A total of 20 hidden nodes network structure produced the best results at 500 iterations (90 percent accuracy based on overall accuracy and 87.50 percent based on κ coefficient). So, 20 hidden nodes are selected for further analysis. The learning rate is set to 0.1, and momentum term used is 0.2 that give the best results architectures. The confusion matrix also shows the accuracy of the classifier. Hence, with these results the proposed system can be used efficiently for more objects. Originality/value After calculating the variation of overall accuracy with different network architectures, the results of different configuration of the sample size of 50 testing images are taken. Table II shows the results of the confusion matrix obtained on these testing samples of objects.

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