SSE-Ship: A SAR Image Ship Detection Model with Expanded Detection Field of View and Enhanced Effective Feature Information  

作　　者：Liping Zheng Liang Tan Liangjun Zhao Feng Ning Bo Xiao Yang Ye Liping Zheng;Liang Tan;Liangjun Zhao;Feng Ning;Bo Xiao;Yang Ye(College of Computer Science and Engineering, Sichuan University of Light Chemical Industry, Zigong, China;College of Resources, Sichuan Agricultural University, Ya’an, China)

机构地区：[1]College of Computer Science and Engineering, Sichuan University of Light Chemical Industry, Zigong, China [2]College of Resources, Sichuan Agricultural University, Ya’an, China

出　　处：《Open Journal of Applied Sciences》2023年第4期562-578,共17页应用科学（英文）

摘　　要：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.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.

关 键 词：Ship Detection SSE-Ship STCSPB SE Attention 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]