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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
