Study on Improved VGGNet and SK Convolution Identification Model for Defect Classification of Single Molten Salt Battery-Reference-Cited by-同舟云学术

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Study on Improved VGGNet and SK Convolution Identification Model for Defect Classification of Single Molten Salt Battery

Published:2023 Issue: Volume:11 Page:48332-48346
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Xu Wenchao¹^ORCID,Xu Wenbo²,Zhang Sixiang¹,Han Yitao³,Jiang Yin³,Zhao Hengyi³

Affiliation:

1. College of Mechanical Engineering, Hebei University of Technology, Tianjin, China

2. College of Higher Vocational Education, Tianjin University of Technology and Education, Tianjin, China

3. College of Information Engineering, Tianjin University of Commerce, Tianjin, China

Funder

National Natural Science Foundation of China

College Students’ Innovative Entrepreneurial Training Plan Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10119143.pdf?arnumber=10119143

Reference48 articles.

1. A Contrast Adjustment Thresholding Method for Surface Defect Detection Based on Mesoscopy

2. Region-Based Convolutional Networks for Accurate Object Detection and Segmentation

3. An improved version of Otsu's method for segmentation of weld defects on X-radiography images

4. Deep Residual Learning for Image Recognition

5. Calculation of flexible printed circuit boards (FPC) global and local defect detection based on computer vision

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