Component recognition method based on deep learning and machine vision

Classification and Recognition of Electronic Components Based on Improved Cooperative Semi-supervised Learning Algorithm

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096514666201224125653 ◽

2020 ◽

Vol 14 ◽

Author(s):

Dan Luo

Keyword(s):

Deep Learning ◽

Machine Vision ◽

Supervised Learning ◽

Image Recognition ◽

Production Efficiency ◽

Learning Algorithm ◽

Electronic Components ◽

Electron Device ◽

Actual Recognition ◽

The Subject

Background: As known that the semi-supervised algorithm is a classical algorithm in semi-supervised learning algorithm. Methods: In the paper, it proposed improved cooperative semi-supervised learning algorithm, and the algorithm process is presented in detailed, and it is adopted to predict unlabeled electronic components image. Results: In the experiments of classification and recognition of electronic components, it show that through the method the accuracy the proposed algorithm in electron device image recognition can be significantly improved, the improved algorithm can be used in the actual recognition process . Conclusion: With the continuous development of science and technology, machine vision and deep learning will play a more important role in people's life in the future. The subject research based on the identification of the number of components is bound to develop towards the direction of high precision and multi-dimension, which will greatly improve the production efficiency of electronic components industry.

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Named Entity Recognition Method for Fault Knowledge based on Deep Learning

Proceedings of the 4th International Conference on Machine Learning and Soft Computing ◽

10.1145/3380688.3380690 ◽

2020 ◽

Author(s):

Zhicheng Chen ◽

Xiaobao Liu ◽

Yanchao Yin ◽

Hongbiao Lu

Keyword(s):

Deep Learning ◽

Named Entity Recognition ◽

Entity Recognition ◽

Recognition Method ◽

Named Entity ◽

Knowledge Based

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Wireless Channel Scene Recognition Method Based on an Autocorrelation Function and Deep Learning

IEEE Access ◽

10.1109/access.2020.3044167 ◽

2020 ◽

Vol 8 ◽

pp. 226324-226336

Author(s):

Shuguang Ning ◽

Yigang He ◽

Lifen Yuan ◽

Yuan Huang ◽

Shudong Wang ◽

...

Keyword(s):

Deep Learning ◽

Autocorrelation Function ◽

Wireless Channel ◽

Scene Recognition ◽

Recognition Method

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A non-destructive and highly efficient model for detecting the genuineness of maize variety 'JINGKE 968′ using machine vision combined with deep learning

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2021.106002 ◽

2021 ◽

Vol 182 ◽

pp. 106002

Author(s):

Keling Tu ◽

Shaozhe Wen ◽

Ying Cheng ◽

Tingting Zhang ◽

Tong Pan ◽

...

Keyword(s):

Deep Learning ◽

Machine Vision ◽

Highly Efficient ◽

Maize Variety ◽

Non Destructive

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An Electronic Component Recognition Algorithm Based on Deep Learning with a Faster SqueezeNet

Mathematical Problems in Engineering ◽

10.1155/2020/2940286 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Yuanyuan Xu ◽

Genke Yang ◽

Jiliang Luo ◽

Jianan He

Keyword(s):

Deep Learning ◽

Characteristic Curve ◽

Area Under The Curve ◽

Principal Component ◽

Recognition Algorithm ◽

Electronic Component ◽

Support Vector ◽

Learning Networks ◽

Component Recognition ◽

And Performance

Electronic component recognition plays an important role in industrial production, electronic manufacturing, and testing. In order to address the problem of the low recognition recall and accuracy of traditional image recognition technologies (such as principal component analysis (PCA) and support vector machine (SVM)), this paper selects multiple deep learning networks for testing and optimizes the SqueezeNet network. The paper then presents an electronic component recognition algorithm based on the Faster SqueezeNet network. This structure can reduce the size of network parameters and computational complexity without deteriorating the performance of the network. The results show that the proposed algorithm performs well, where the Receiver Operating Characteristic Curve (ROC) and Area Under the Curve (AUC), capacitor and inductor, reach 1.0. When the FPR is less than or equal 10 − 6 level, the TPR is greater than or equal to 0.99; its reasoning time is about 2.67 ms, achieving the industrial application level in terms of time consumption and performance.

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