Mr. Yang Tang Profile

Yang Tang

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 | 27 March 2024 Paper

Infrared-radar multimodal target detection algorithm combining position prior information

Xinkai Zhi, Hanqing Zhou, Yang Tang

Proceedings Volume 13105, 131053H (2024) https://doi.org/10.1117/12.3026382

KEYWORDS: Object detection, Target detection, Detection and tracking algorithms, Infrared radiation, Radar sensor technology, Radar, Infrared detectors, Feature extraction, Infrared imaging, Performance modeling

Read Abstract +

In recent years, multimodal composite guidance technology has received extensive research and exploration, and multimodal fusion methods based on deep learning have achieved performance far superior to traditional methods. However, in practical applications, there are still many difficulties in the fusion technology of infrared images and high-resolution range profiles. In response to the above challenges, this paper fully utilizes the characteristics of sensor data collection to conduct in-depth research on the application of position prior, cross modal attention mechanism, and domain adaptation in multimodal object detection problems. A multimodal object detection algorithm called CenterNet- PK, which introduces a position prior, is proposed to deeply fuse infrared radar feature information while alleviating data heterogeneity issues. A feature extraction module based on one-dimensional convolution and bidirectional gated cyclic unit (Bi-GRU) was designed, and a radar feature map generation algorithm based on position prior was proposed. A cascade strategy was used to deeply fuse infrared and radar feature maps. Finally, the improved adaptive key point loss function was used to complete network training. The experimental results show that the proposed algorithm has high detection accuracy and anti-interference ability.

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