TITLE:
SSE-Ship: A SAR Image Ship Detection Model with Expanded Detection Field of View and Enhanced Effective Feature Information
AUTHORS:
Liping Zheng, Liang Tan, Liangjun Zhao, Feng Ning, Bo Xiao, Yang Ye
KEYWORDS:
Ship Detection, SSE-Ship, STCSPB, SE Attention
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.13 No.4,
April
28,
2023
ABSTRACT: In this paper, we propose a SAR image ship detection model SSE-Ship that
combines image context to extend the detection field of view domain and
effectively enhance feature extraction information. This method aims to solve
the problem of low detection rate in SAR images with ship combination and ship
fusion scenes. Firstly, we propose STCSPB network to solve the problem of ship and non-ship object fusion by combining
image contextual feature information to distinguish ship and non-ship
objects. Secondly, we combine SE Attention to enhance the effective feature information and effectively
improve the detection accuracy in combined ship driving scenes. Finally, we
conducted extensive experiments on two standard base datasets, SAR-Ship and
SSDD, to verify the effectiveness and stability of our proposed method. The experimental results show that
the SSE-Ship model has P = 0.950, R = 0.946, mAP_0.5:0.95 = 0.656 and FPS = 50
on the SAR-Ship dataset and mAP_0.5 = 0.964 and R = 0.940 on the SSDD
dataset.