Mr. Jaeyeob Kim Profile

Jaeyeob Kim

at LIG Nex1 Co Ltd

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 | 13 June 2023 Presentation + Paper

Unsupervised domain adaptation for multispectral object detection

Hyunsung Jang, Minseok Lee, Jaeyeob Kim, Namkoo Ha, Kwanghoon Sohn

Proceedings Volume 12521, 125210Q (2023) https://doi.org/10.1117/12.2664074

KEYWORDS: Artificial intelligence, Deep learning, Image processing, Automatic target recognition, Object detection, Multispectral imaging, Target detection

Read Abstract +

Recent advancements in image sensor technology and deep learning have led to increased expectations for improving object detection performance in EO/IR systems. However, the large-scale datasets made available for deep learning model training exhibit significant differences in data distribution compared to the multispectral images acquired from the actual target domain, EO/IR systems. Because of this, when a pretrained deep-learning model is applied to real-world issues for multispectral object detection, it does not work well for inputs with a different distribution from a training dataset. Therefore, a dataset for additional fine-tuning is required. However, labeled datasets required for retraining can be challenging to acquire in practice because of security issues and high acquisition costs. These domain differences and dataset issues are crucial challenges concerning the use of AI in military applications. We herein propose an unsupervised domain adaptation method for object detection in multispectral images with domain discrepancies with a pretrained model on a large public dataset. We train an encoder network without direct labeling to generate feature maps similar to the backbone applied to existing detectors. The proposed approach has the advantage of being able to directly utilize pretrained object detection models from large-scale public datasets by simply training the encoder network alone.

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