Defect detection method using deep convolutional neural network, support vector machine and template matching techniques

2019 ◽  
Vol 24 (4) ◽  
pp. 512-519 ◽  
Author(s):  
Fusaomi Nagata ◽  
Kenta Tokuno ◽  
Kazuki Mitarai ◽  
Akimasa Otsuka ◽  
Takeshi Ikeda ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Defect Detection ◽  
Template Matching ◽  
Detection Method ◽  
Deep Convolutional Neural Network ◽  
Support Vector ◽  
Network Support ◽  
Matching Techniques

scholarly journals Research on Automated Defect Classification Based on Visual Sensing and Convolutional Neural Network-Support Vector Machine for GTA-Assisted Droplet Deposition Manufacturing Process

Metals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 639
Author(s):  
Chen Ma ◽  
Haifei Dang ◽  
Jun Du ◽  
Pengfei He ◽  
Minbo Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Manufacturing Process ◽  
Support Vector ◽  
Defect Classification ◽  
Droplet Deposition ◽  
Network Support ◽  
Visual Sensing ◽  
Low Efficiency

This paper proposes a novel metal additive manufacturing process, which is a composition of gas tungsten arc (GTA) and droplet deposition manufacturing (DDM). Due to complex physical metallurgical processes involved, such as droplet impact, spreading, surface pre-melting, etc., defects, including lack of fusion, overflow and discontinuity of deposited layers always occur. To assure the quality of GTA-assisted DDM-ed parts, online monitoring based on visual sensing has been implemented. The current study also focuses on automated defect classification to avoid low efficiency and bias of manual recognition by the way of convolutional neural network-support vector machine (CNN-SVM). The best accuracy of 98.9%, with an execution time of about 12 milliseconds to handle an image, proved our model can be enough to use in real-time feedback control of the process.

Convolutional neural network - Support vector machine based approach for classification of cyanobacteria and chlorophyta microalgae groups

2021 ◽  
pp. 102568
Author(s):  
Mesut Ersin Sonmez ◽  
Numan Eczacıoglu ◽  
Numan Emre Gumuş ◽  
Muhammet Fatih Aslan ◽  
Kadir Sabanci ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Support Vector ◽  
Network Support

scholarly journals An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Wentao Wu ◽  
Daning Li ◽  
Jiaoyang Du ◽  
Xiangyu Gao ◽  
Wen Gu ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Brain Segmentation ◽  
Brain Tumor Segmentation ◽  
Segmentation Methods

Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In the brain tumor segmentation method based on deep learning, the convolutional network model has a good brain segmentation effect. The deep convolutional network model has the problems of a large number of parameters and large loss of information in the encoding and decoding process. This paper proposes a deep convolutional neural network fusion support vector machine algorithm (DCNN-F-SVM). The proposed brain tumor segmentation model is mainly divided into three stages. In the first stage, a deep convolutional neural network is trained to learn the mapping from image space to tumor marker space. In the second stage, the predicted labels obtained from the deep convolutional neural network training are input into the integrated support vector machine classifier together with the test images. In the third stage, a deep convolutional neural network and an integrated support vector machine are connected in series to train a deep classifier. Run each model on the BraTS dataset and the self-made dataset to segment brain tumors. The segmentation results show that the performance of the proposed model is significantly better than the deep convolutional neural network and the integrated SVM classifier.

A Hybrid Approach For Automatic Licence Plate Recognition System

2020 ◽  
Vol 10 ◽  
Author(s):  
Nitin Sharma ◽  
Pawan Kumar Dahiya ◽  
Baldev Raj Marwah
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Recognition System ◽  
Traffic Monitoring ◽  
Support Vector ◽  
Network Support ◽  
Recognition Systems ◽  
Licence Plate Recognition

: Automatic licence plate recognition systems are used for various applications such as traffic monitoring, toll collection, car parking, law enforcement. In this paper, a convolutional neural network and support vector machine based automatic licence plate recognition system is proposed. Firstly, The characters extracts from the input image of vehicle. Then characters are segment and their features are extracts. The extracted features are classified using convolutional neural network and support vector machine for the final recognition of the licence plate. The obtained recognition rate by the hybridization of the convolutional neural network and the support vector machine is 96.5%. The recognition rate obtained for the proposed hybrid automatic licence plate system are compared with three other automatic licence plate systems based on neural network, support vector machine, and convolutional neural network. The proposed automatic licence plate recognition system perform better than the neural network, support vector machine, and convolutional nerural network based automatic licence plate recognition systems.

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