A Novel Defect Inspection Approach Using Image Processing and Support Vector Machines in Bolts

2015 ◽  
Author(s):  
Xiao Lv
Keyword(s):  
Image Processing ◽  
Support Vector Machines ◽  
Support Vector ◽  
Defect Inspection ◽  
Vector Machines

scholarly journals On Combining Convolutional Autoencoders and Support Vector Machines for Fault Detection in Industrial Textures

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3339
Author(s):  
Alberto Tellaeche Iglesias ◽  
Miguel Ángel Campos Anaya ◽  
Gonzalo Pajares Martinsanz ◽  
Iker Pastor-López
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Support Vector Machines ◽  
Ad Hoc ◽  
Research Work ◽  
Image Sensors ◽  
Great Variability ◽  
Support Vector ◽  
Vector Machines ◽  
Textured Materials

Defects in textured materials present a great variability, usually requiring ad-hoc solutions for each specific case. This research work proposes a solution that combines two machine learning-based approaches, convolutional autoencoders, CA; one class support vector machines, SVM. Both methods are trained using only defect free textured images for each type of analyzed texture, labeling the samples for the SVMs in an automatic way. This work is based on two image processing streams using image sensors: (1) the CA first processes the incoming image from the input to the output, producing a reconstructed image, from which a measurement of correct or defective image is obtained; (2) the second process uses the latent layer information as input to the SVM to produce a measurement of classification. Both measurements are effectively combined, making an additional research contribution. The results obtained achieve a percentage of success of 92% on average, outperforming results of previous works.

Mobile-device Based Image Processing for Rice Brown Planthopper Classification and Outbreak Monitoring

2019 ◽  
Vol 35 (1) ◽  
pp. 15-21
Author(s):  
Sarin Watcharabutsarakham ◽  
Ithipan Methasate
Keyword(s):  
Image Processing ◽  
Support Vector Machines ◽  
Mobile Device ◽  
Rice Field ◽  
Field Experiments ◽  
Life Stage ◽  
Brown Planthopper ◽  
Image Features ◽  
Support Vector ◽  
Vector Machines

Abstract.The rice brown planthopper (BPH) outbreak is one of several causes of damage to rice crops in Thailand. A traditional way to monitor the early outbreak is to routinely and randomly count the density of BPHs spreading around the rice field. This article presents an assistive tool to monitor the BPH by using automatic image processing. Smart phone devices with a sufficient camera quality are currently affordable and convenient for farmers to capture images from their rice fields. Based on the Support Vector Machines algorithm trained on color and Gray Level Co-occurrence Matrix (GLCM) image features, the proposed system not only automatically detects the position of BPHs in the collected images, but is also able to classify the life stage of each hopper. The use of a red-frame mark on the camera screen to guide BPH image capturing helps improving the overall processing accuracy. Field experiments with the Rice Department of the Ministry of Agriculture and Cooperatives of Thailand shows the proposed system achieved an approximately 89% detection F-measure and an 87% BPH life-stage classification accuracy. Moreover, this article illustrates the preciseness of BPH density prediction with respect to the different numbers of sampling images from the rice field. The result suggests farmers to take at least 40 images per 1,600 square meters in order to gain more than 87% prediction accuracy. Keywords: BPH classification, BPH life-stage, Brown planthopper (BPH), Image processing, Mobile device, Support vector machines, Rice.

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