摘要
合成孔径雷达(synthetic aperture radar,SAR)图像场景复杂度高、舰船目标尺度小,传统方法检测效率低、虚警概率大。针对以上问题,提出一种特征增强网络用于SAR图像舰船目标检测。首先,利用I-Darknet-53(improved Darknet-53)提取特征信息,构建4层特征金字塔丰富低层特征。其次,将多个特征层进行跨尺度连接,使低层细节信息更易于向高层语义信息映射,增强特征的传播和重用。最后,利用多尺度注意力模型增强特征信息,为检测器提供高质量的判断依据。试验结果表明,所提算法在SSDD数据集上的平均检测精度为95%。相较于其他网络模型,所提算法具有明显优势。
Traditional detection methods are inefficient and have a high probability of false alarm due to the high complexity of synthetic aperture radar(SAR)image scenes and small scale of ship targets.To address these problems,this paper proposes a feature-enhanced network for SAR image ship target detection is proposed.Firstly,feature information is extracted using I-Darknet53(improved Darknet-53),and a four-layer feature pyramid is constructed to enrich low-level features.Secondly,multiple feature layers are connected across scales to make low-level detail information easier to map to high-level semantic information,thus enhancing the propagation and reuse of features.Finally,the feature information is enhanced using a multi-scale attention model to provide a high-quality judgment basis for the detector.The experimental results show that the average detection accuracy of the proposed algorithm on the SSDD dataset is 95%.The proposed algorithm has high precision compared with other network models.
作者
张冬冬
王春平
付强
ZHANG Dongdong;WANG Chunping;FU Qiang(Department of Electronic and Optical Engineering,Army Engineering University Shijiazhuang Campus,Shijiazhuang 050003,China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2023年第4期1032-1039,共8页
Systems Engineering and Electronics
关键词
合成孔径雷达图像
目标检测
特征增强
多尺度融合
多尺度注意力
synthetic aperture radar(SAR)image
target detection
feature enhancement
multi-scale fusion
multi-scale attention
