Ms. Jie Zhu Profile

Jie Zhu

SPIE Involvement:

Author

Publications (1)

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 | 30 April 2024 Paper

Classification algorithm for underwater surface cracks in hydroelectric dams

Hongzhu Chen, Jin Qian, Long Peng, Jie Zhu, Sisi Zhu, Xinyu Li, Pengfei Cao, Pengfei Shi

Proceedings Volume 13156, 131560J (2024) https://doi.org/10.1117/12.3015336

KEYWORDS: Image classification, Feature extraction, Network architectures, Evolutionary algorithms, Convolution, Computer vision technology

Read Abstract +

Hydroelectric dams have a vital role to play in renewable energy and water resources, and ensuring the safety and structural integrity of these dams is crucial. Cracks are a major disease hazard threatening the safety of dams, usually originating on the surface of the dam and continuously extending inwards under the action of hydraulic splitting. Timely and accurately detecting and identifying dam surface cracks is important for maintaining dam safety. However, due to the complexity of the underwater environment and the diversity of dam surface cracks, manual observation methods are time-consuming and laborious to classify underwater surface crack images of dams, and the existing methods have low recognition accuracy when used for the crack classification task. For this reason, this paper proposes an algorithm for classifying underwater surface cracks in hydroelectric dams, which classifies the cracks extracted from the image segmentation model of underwater cracks in hydroelectric dams with the help of an improved RepVGG network. A CBAM attention mechanism module is inserted into the RepVGG feature extraction network to extract a more effective feature representation. Due to the imbalance problem of data samples, the class balancing loss function of IB Loss is introduced to achieve class balancing. The experimental results show that for different kinds of cracks, the accuracy of the constructed algorithms is higher than the classical image classification algorithms, and the average classification accuracy of the network is improved by 2.06% compared to the original RepVGG algorithm, which is improved compared to all other traditional classification networks.

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