Dr. Qingbin Gao Profile

Dr. Qingbin Gao

at Harbin Institute of Technology

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 9 May 2024 Presentation + Paper

Inspection of wind turbine blades using image deblurring and deep learning segmentation

Jiale Lu, Qingbin Gao, Kai Zhou

Proceedings Volume 12951, 129510L (2024) https://doi.org/10.1117/12.3009721

KEYWORDS: Deblurring, Image segmentation, Image quality, Wind turbine technology, Motion blur, Education and training, Inspection, Image processing, Deep learning, Motion models

Read Abstract +

Remote and complex work sites of wind turbines limit the accessibility of the condition assessment. Wind turbine blades are subject to sustained wind load and harsh natural environmental conditions, which are vulnerable to various faults. Robotic-enabled sensing technology appears to be a promising solution for an efficient wind turbine blade inspection. Together with the recent advances in image processing and deep learning segmentation, automated inspection of wind turbine blades becomes possible. Nevertheless, it remains a challenging task to quantify the damage accurately due to the complex condition of images concerning motion blurs. To address this issue, an integrated framework, i.e., the combination of a Deblur Generative Adversarial Network v2 (DeblurGAN-v2) and You Only Look Once v8 (YOLO-v8) was proposed in this study. Specifically, the mapping between the motion-blurred images and those in high quality was adopted from the open-access pretrained DeblurGAN-v2, based on which the deblurring performance for wind turbine images with various motion blur scales was discussed concerning the image quality. Subsequently, the transfer learning method was implemented to fine-tune YOLO-v8. The well-trained YOLO v8 was then utilized for target defect segmentation on the deblurred images. Finally, various metrics were calculated to evaluate the segmentation accuracy and efficiency. Results prove a good generalization of DeblurGAN-v2 on wind turbine images and clearly illustrate the enhanced performance of the proposed methodology especially when the motion blur scale is within 35. The integrated framework could serve as a reference for dealing with other fuzzy image-related issues.

Proceedings Article | 9 May 2024 Presentation + Paper

Physics-guided data-driven failure identification of underwater mooring systems in offshore infrastructures

Yixuan Liu, Shangyan Zou, Qingbin Gao, Kai Zhou

Proceedings Volume 12951, 1295110 (2024) https://doi.org/10.1117/12.3009646

KEYWORDS: Data modeling, Education and training, Performance modeling, Convolution, Modeling, Systems modeling, Failure analysis, Oceanography, Feature extraction, System identification

Read Abstract +

Many offshore infrastructures have been developed to explore vast marine resources over the past several decades. In addition to the conventional fixed-type offshore infrastructures, a new class of offshore infrastructures, the so-called floating offshore infrastructures, have gained dramatically increasing applications owing to their flexible deployment and enhanced capacity in renewable energy exploitation in deep seawater. As the key functional component of the floating infrastructure, the underwater mooring systems are subject to sustained dynamic loads pertinent to marine waves and currents, which are prone to different types of failures. Identifying those mooring system failures timely and reliably thus plays a vital role in offshore infrastructure health management and maintenance. This study aims to achieve this objective by developing an integrated numerical framework that seamlessly synthesizes the physical mooring system modeling and data-driven analysis. Specifically, a high-fidelity physical model that takes into account the sophisticated fluid-structure interaction is established to mimic the underlying behavior of the mooring system. The mooring line failures are incorporated into the model to generate the respective dynamic responses. With the aid of data-driven modeling, the causative relationship between mooring line failure scenarios and dynamic responses can be characterized. Given the sensor measurement in actual practice, this framework offers a feasible solution for the failure identification of underwater mooring systems. The results clearly demonstrate the feasibility of the proposed methodology.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years