Wind Turbine Actual Defects Detection Based on Visible and Infrared Image Fusion-Reference-Cited by-同舟云学术

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Wind Turbine Actual Defects Detection Based on Visible and Infrared Image Fusion

Published:2023 Issue: Volume:72 Page:1-8
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Zhou Weijie¹^ORCID,Wang Zijun²^ORCID,Zhang Minshu¹^ORCID,Wang Liwen³^ORCID

Affiliation:

1. School of Materials Science and Engineering, Xiamen University of Technology, Xiamen, China

2. School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China

3. Academy of Science and Technology, Dongfang Electric Corporation, Ltd., Chengdu, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of SiChuan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10057459.pdf?arnumber=10057459

Reference31 articles.

1. Modality-correlation-aware sparse representation for RGB-infrared object tracking

2. Learning Modality-Consistency Feature Templates: A Robust RGB-Infrared Tracking System

3. R-CNN for Small Object Detection

4. Object fusion tracking based on visible and infrared images: A comprehensive review

5. Rethinking the inception architecture for computer vision;szegedy;arXiv 1512 00567 [cs],2015

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1. Wind Turbine Blade Erosion Detection using Visual Inspection and Transfer Learning;2024 International Conference on Control, Automation and Diagnosis (ICCAD);2024-05-15

2. A feature refinement and adaptive generative adversarial network for thermal infrared image colorization;Neural Networks;2024-05

3. An Infrared Image Defect Detection Method for Steel Based on Regularized YOLO;Sensors;2024-03-05

4. Data Fusion and Ensemble Learning for Advanced Anomaly Detection Using Multi-Spectral RGB and Thermal Imaging of Small Wind Turbine Blades;Energies;2024-01-31

5. Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detection;IEEE Access;2024

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