摘要
目标检测算法在PASCAL VOC等数据集中取得了非常好的检测效果,但是在大尺度遥感图像中舰船目标的检测准确率却很低。因此,针对可见光遥感图像的特点,在YOLOv3-Tiny算法的基础上增加了特征映射模块,为预测层提供丰富的语义信息,同时在特征提取网络中引用残差网络,提高了检测准确率,从而有效提取舰船特征。实验结果表明:优化后的M-YOLO算法检测准确率为94.12%。相比于SSD和YOLOv3算法,M-YOLO算法的检测准确率分别提高了11.11%和9.44%。
Although the target detection algorithm has achieved very good detection results in data sets such as PASCAL VOC.However, the accuracy of ship target detection in large-scale prediction images is very low.Therefore, according to the characteristics of the visible light reflection image, a feature mapping module is added on the basis of the YOLOv3-Tiny algorithm, which provides rich semantic information for the prediction layer.At the same time, a residual network is used in the feature extraction network, which improves the detection accuracy and effectively extracts ship features. Experimental results show that the detection accuracy of the optimized M-YOLO algorithm is 94.12%.Compared with the SSD and YOLOv3 algorithms, the detection accuracy of the M-YOLO algorithm is improved by 11.11% and 9.44%.
作者
王玺坤
姜宏旭
林珂玉
WANG Xikun;JIANG Hongxu;LIN Keyu(Beijing Key Laboratory of Digital Media,Beihang University,Beijing 100083,China)
出处
《北京航空航天大学学报》
EI
CAS
CSCD
北大核心
2020年第6期1184-1191,共8页
Journal of Beijing University of Aeronautics and Astronautics
基金
国家自然科学基金(61872017)
航天科学技术基金(190109)。
