Automatic visual defects inspection of wind turbine blades via YOLO-based small object detection approach-Reference-Cited by-同舟云学术

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Automatic visual defects inspection of wind turbine blades via YOLO-based small object detection approach

Published:2019-08-06 Issue:04 Volume:28 Page:1
ISSN:1017-9909
Container-title:Journal of Electronic Imaging
language:
Short-container-title:J. Electron. Imag.

Author:

Qiu Zifeng¹,Wang Shuangxin¹,Zeng Zhaoxi²,Yu Dingli³

Affiliation:

1. Beijing Jiaotong University, School of Mechanical, Electronic and Control Engineering, Beijing

2. University of Pennsylvania, Graduate School of Education, Philadelphia, Pennsylvania

3. Liverpool John Moores University, School of Engineering, Control Systems Center, Liverpool

Publisher

SPIE-Intl Soc Optical Eng

Subject

Electrical and Electronic Engineering,Computer Science Applications,Atomic and Molecular Physics, and Optics

Reference20 articles.

1. Transformation algorithm of wind turbine blade moment signals for blade condition monitoring

2. Hybrid Signal Processing Technique to Improve the Defect Estimation in Ultrasonic Non-Destructive Testing of Composite Structures

3. In-Situ Cure Monitoring of Wind Turbine Blades by Using Fiber Bragg Grating Sensors and Fresnel Reflection Measurement

4. Wavelet transform-based stress-time history editing of horizontal axis wind turbine blades

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1. Wind turbine blade defect detection with a semi-supervised deep learning framework;Engineering Applications of Artificial Intelligence;2024-10

2. UAV based defect detection and fault diagnosis for static and rotating wind turbine blade: a review;Nondestructive Testing and Evaluation;2024-08-28

3. Improved YOLOv8-CR Network for Detecting Defects of the Automotive MEMS Pressure Sensors;IEEE Sensors Journal;2024-08-15

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

5. Inspection of wind turbine blades using image deblurring and deep learning segmentation;Health Monitoring of Structural and Biological Systems XVIII;2024-05-09

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