Scale and rotational invariant deep neural machine for ship detections in infrared images

Wen P. Zhu; Ruixu Liu; Theus Aspiras; Vijayan K. Asari

doi:10.1117/12.2664879

13 June 2023 Scale and rotational invariant deep neural machine for ship detections in infrared images

Wen P. Zhu, Ruixu Liu, Theus Aspiras, Vijayan K. Asari

Proceedings Volume 12527, Pattern Recognition and Tracking XXXIV; 125270N (2023) https://doi.org/10.1117/12.2664879
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Automatic ship detections in complex background during the day and night in infrared images is an important task. Additionally, we want to have the capability to detect the ships in various scales, orientations, and shapes. In this paper, we propose the use of neural network technology for this purpose. The algorithm used for this task is the Deep Neural Machine (DNM), which contains three different parts (backbone, neck, and head). Combining all three steps, this algorithm can extract the features, create prediction layers using different scales of the backbone, and give object predictions at different scales. The experimental results show that our algorithm is robust and efficient in detecting ships in complex background.

Conference Presentation

Citation Download Citation

Wen P. Zhu, Ruixu Liu, Theus Aspiras, and Vijayan K. Asari "Scale and rotational invariant deep neural machine for ship detections in infrared images", Proc. SPIE 12527, Pattern Recognition and Tracking XXXIV, 125270N (13 June 2023); https://doi.org/10.1117/12.2664879

ACCESS THE FULL ARTICLE

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