LFRNet: Localizing, Focus, and Refinement Network for Salient Object Detection of Surface Defects-Reference-Cited by-同舟云学术

LFRNet: Localizing, Focus, and Refinement Network for Salient Object Detection of Surface Defects

Published:2023 Issue: Volume:72 Page:1-12
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Wan Bin¹^ORCID,Zhou Xiaofei¹^ORCID,Zheng Bolun¹^ORCID,Yin Haibing²^ORCID,Zhu Zunjie²^ORCID,Wang Hongkui²^ORCID,Sun Yaoqi³^ORCID,Zhang Jiyong¹^ORCID,Yan Chenggang¹^ORCID

Affiliation:

1. School of Automation, Hangzhou Dianzi University, Hangzhou, China

2. Lishui Institute and the School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China

3. Lishui Institute and the School of Automation Engineering, Hangzhou Dianzi University, Hangzhou, China

Funder

National Key Research and Development Program of China

Fundamental Research Funds for the Provincial Universities of Zhejiang

National Nature Science Foundation of China

“Pioneer” and “Leading Goose” Research and Development (R&D) Program of Zhejiang Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10056257.pdf?arnumber=10056257

Reference71 articles.

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

2. Saliency filters: Contrast based filtering for salient region detection

3. Fabric Defect Detection Using Modified Local Binary Patterns

4. RMSPROP: Divide the gradient by a running average of its recent magnitude;hinton;Neural Netw Mach Learn Coursera Lect,2012

5. Dense Attention-guided Cascaded Network for Salient Object Detection of Strip Steel Surface Defects

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