北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (6): 1184-1191.doi: 10.13700/j.bh.1001-5965.2019.0394

• 论文 • 上一篇    下一篇

基于改进型YOLO算法的遥感图像舰船检测

王玺坤, 姜宏旭, 林珂玉   

  1. 北京航空航天大学 数字媒体北京市重点实验室, 北京 100083
  • 收稿日期:2019-07-19 发布日期:2020-07-02
  • 通讯作者: 姜宏旭 E-mail:jianghx@buaa.edu.cn
  • 作者简介:王玺坤 男,硕士研究生。主要研究方向:嵌入式图像处理;姜宏旭 男,博士,研究员,博士生导师。主要研究方向:智能硬件、嵌入式图像处理;林珂玉 女,硕士研究生。主要研究方向:嵌入式图像处理。
  • 基金资助:
    国家自然科学基金(61872017);航天科学技术基金(190109)

Remote sensing image ship detection based on modified YOLO algorithm

WANG Xikun, JIANG Hongxu, LIN Keyu   

  1. Beijing Key Laboratory of Digital Media, Beihang University, Beijing 100083, China
  • Received:2019-07-19 Published:2020-07-02
  • Supported by:
    National Key R & D Program of China (2018YFB0905500,2018YFB0905503); the Key Fund for Equipment Pre-research of China (61407210206)

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%。

关键词: 舰船检测, YOLOv3, YOLOv3-Tiny, 残差网络, 特征映射模块

Abstract: 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%.

Key words: ship detection, YOLOv3, YOLOv3-Tiny, residual network, feature mapping module

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