ADDet: An Efficient Multiscale Perceptual Enhancement Network for Aluminum Defect Detection-Reference-Cited by-同舟云学术

ADDet: An Efficient Multiscale Perceptual Enhancement Network for Aluminum Defect Detection

Published:2024 Issue: Volume:73 Page:1-14
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Zhu Jiang¹^ORCID,Pang Qingwei¹^ORCID,Li Saisi¹^ORCID,Tian Shujuan²^ORCID,Li Jianqi³^ORCID,Li Yanchun²^ORCID

Affiliation:

1. School of Automation and Electronic Information, Xiangtan University, Xiangtan, China

2. Hunan International Scientific and Technological Cooperation Base of Intelligent Network and the Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China

3. Hunan Provincial Key Laboratory for Control Technology of Distributed Electric Propulsion Aircraft, Changde, China

Funder

National Natural Science Foundation of China

Hunan Province Natural Science Foundation of China

Hunan Provincial Innovation Foundation for Postgraduate

Research Foundation of Education Bureau of Hunan Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10363379.pdf?arnumber=10363379

Reference45 articles.

1. Distinctive Image Features from Scale-Invariant Keypoints

2. Histograms of Oriented Gradients for Human Detection

3. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns

4. A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects

5. Defect Classification With SVM and Wideband Excitation in Multilayer Aluminum Plates

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