Assembly Defect Classification of SMD Components by Cascade Convolutional Neural Network

2019 ◽  
Vol 68 (10) ◽  
pp. 1236-1243
Author(s):  
Jong-Hyun Ryu ◽  
Young-Gyu Kim ◽  
Tae-Hyoung Park
Author(s):  
Rindi Kusumawardani ◽  
Putu Dana Karningsih

Packaging is one of the important aspects of a product’s identity. The good and adorable packaging can increase product competitiveness because it gives a perception to the customers of good quality products. Therefore, a good packaging display is necessary so that packaging quality inspection is very important. Automated defect detection can help to reduce human error in the inspection process. Convolutional Neural Network (CNN) is an approach that can be used to detect and classify a packaging condition. This paper presents an experiment that compares 5 network models, i.e. ShuffleNet, GoogLeNet, ResNet18, ResNet50, and Resnet101, each network given the same parameters. The dataset is an image of cans packaging which is divided into 3 classifications, No Defect, Minor Defect, and Major Defect. The experimental result shows that network architecture models of ResNet50 and ResNet101 provided the best result for cans defect classification than the other network models, with 95,56% for testing accuracy. The five models have the testing accuracy above 90%, so it can be concluded that all network models are ideal for detecting the packaging defect and defect classification for the cans product.


2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.


Author(s):  
P.L. Nikolaev

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.


2020 ◽  
Vol 14 ◽  
Author(s):  
Lahari Tipirneni ◽  
Rizwan Patan

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.


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