I²GF-Net: Interlayer Information Guidance Feedback Networks for Wood Surface Defect Detection in Complex Texture Backgrounds-Reference-Cited by-同舟云学术

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I²GF-Net: Interlayer Information Guidance Feedback Networks for Wood Surface Defect Detection in Complex Texture Backgrounds

Published:2024 Issue: Volume:73 Page:1-13
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Luo Qiwu¹^ORCID,Xu Wenlong¹^ORCID,Su Jiaojiao¹^ORCID,Yang Chunhua¹^ORCID,Gui Weihua¹^ORCID,Silvén Olli²^ORCID,Liu Li³^ORCID

Affiliation:

1. School of Automation, Central South University, Changsha, China

2. Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Oulu, Finland

3. College of System Engineering, National University of Defense Technology, Changsha, China

Funder

National Natural Science Foundation of China

Science and Technology Innovation Program of Hunan Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/19/10367905/10570209.pdf?arnumber=10570209

Reference44 articles.

1. An Accurate and Real-Time Surface Defects Detection Method for Sawn Lumber

2. A large-scale image dataset of wood surface defects for automated vision-based quality control processes

3. Detecting Defects on Solid Wood Panels Based on an Improved SSD Algorithm

4. Utilizing adsorption of wood and its derivatives as an emerging strategy for the treatment of heavy metal-contaminated wastewater

5. Top-Down Visual Attention from Analysis by Synthesis

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