Research on Defect Recognition of Ceramic Chips for High Temperature Fuel Cells Based on Improved Faster R-CNN-Reference-Cited by-同舟云学术

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Research on Defect Recognition of Ceramic Chips for High Temperature Fuel Cells Based on Improved Faster R-CNN

Published:2022-02 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Machine Learning and Knowledge Engineering (MLKE)
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Author:

Lu Jiadong¹,Wu Jian¹,Zhang Jianfeng¹,Hu Qiang²,Zhang Guimin³,Sun Xiaomao⁴,Xu Ren⁵

Affiliation:

1. Zhejiang A&F University,College of Mathematics and Computer Science,Hangzhou,China

2. Lishui University,College of Ecology,Lishui,China

3. Zhejiang Zhentai Energy Technology Company Limited,Lishui,China

4. Tsinghua Sichuan Energy Internet Research Institute,Chengdu,China

5. Taizhou Radio and Television University,Taizhou,China

Funder

Research and Development

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9763553/9763490/09763492.pdf?arnumber=9763492

Reference14 articles.

1. Very Deep Convolutional Networks for Large-Scale Image Recognition[J];simonyan;Institute of Electronics and Computer Science,2014

2. Feature Pyramid Networks for Object Detection

3. Mask R-CNN

4. Theoretical analysis of batch and on-line training for gradient descent learning in neural networks

5. A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5[J];bin;Remote Sensing MDPI AG,2021

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