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

Author:

Xu Yuanyuan12ORCID,Yang Genke13ORCID,Luo Jiliang2,He Jianan4

Affiliation:

1. Department of Automation, Shanghai Jiaotong University, Shanghai 200240, China

2. College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China

3. NingBo Artificial Intelligence Institute, Shanghai Jiaotong University, Ningbo 315000, China

4. Central Laboratory of Health Quarantine, Shenzhen International Travel Health Care Center, Shenzhen Academy of Inspection and Quarantine, Shenzhen Customs District, Shenzhen 518033, China

Abstract

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.

Funder

National Key R&D Program of China

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

Reference44 articles.

1. Base and current situation of data standardization for electronic components & devices;B. Wang;Electronic Component & Device Applications,2010

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