SAR ship detection based on YOLOv5

Tian Ming; Yanwei Ju

doi:10.1117/12.2660100

3 February 2023 SAR ship detection based on YOLOv5

Tian Ming, Yanwei Ju

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125111N (2023) https://doi.org/10.1117/12.2660100
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

For the task of SAR ship detection , improvements are made on the basis of YOLOv5. Considering the ship target characteristics, the loss function is improved. And the coordinate attention mechanism (CA) is added to the backbone. Finally, a layer of feature fusion branches is added to the path aggregation network (PANet). Contrast with unchanged YOLOv5 detection network, this improvement increases the precision rate from 93.5% to 96.1%, the recall rate from 93.4% to 95.3%, and the mAP from 93.9% to 97.3%. The network detection performance has been significantly improved.

Citation Download Citation

Tian Ming and Yanwei Ju "SAR ship detection based on YOLOv5", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125111N (3 February 2023); https://doi.org/10.1117/12.2660100

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