The segmentation algorithm of marine warship image based on mask RCNN

Hongyan Chen; Xinle Yu

doi:10.1117/12.2589235

20 January 2021 The segmentation algorithm of marine warship image based on mask RCNN

Hongyan Chen, Xinle Yu

Proceedings Volume 11719, Twelfth International Conference on Signal Processing Systems; 1171904 (2021) https://doi.org/10.1117/12.2589235
Event: Twelfth International Conference on Signal Processing Systems, 2020, Shanghai, China

Abstract

This paper proposes a warship image segmentation algorithm based on Mask RCNN network. Based on the Tensorflow+ Keral deep learning framework, the Mask-RCNN network structure was constructed. The segmentation of the image of warship at sea level was achieved by using the supervised learning method and tagging of the data set. Mask R-CNN is the most advanced convolutional neural network algorithm, which is mainly used for object detection and object instance segmentation of natural images. Due to the difficulty in obtaining warship samples and the insufficient number of data sets, the method of data enhancement is adopted to expand the data set. Through parameter adjustment and experimental verification, the mAP of warship reaches 0.603, which can meet the requirements of high-precision segmentation. The experimental results show that the Mask RCNN model has a very good effect on the image segmentation of naval ships at sea.

Citation Download Citation

Hongyan Chen and Xinle Yu "The segmentation algorithm of marine warship image based on mask RCNN", Proc. SPIE 11719, Twelfth International Conference on Signal Processing Systems, 1171904 (20 January 2021); https://doi.org/10.1117/12.2589235

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