Rotated Region Based CNN for Ship Detection | |
Liu, Zikun1,2; Hu, Jingao1,2; Weng, Lubin1; Yang Yiping1 | |
2017-10 | |
会议名称 | 2017 IEEE International Conference on Image Processing |
会议日期 | 17-20 September 2017 |
会议地点 | China National Convention Center in Beijing, China |
摘要 | The state-of-the-art object detection networks for natural images have recently demonstrated impressive performances. However the complexity of ship detection in high resolution satellite images exposes the limited capacity of these networks for strip-like rotated assembled object detection which are common in remote sensing images. In this paper, we embrace this observation and introduce the rotated region based CNN (RR-CNN), which can learn and accurately extract features of rotated regions and locate rotated objects precisely. RR-CNN has three important new components including a rotated region of interest (RRoI) pooling layer, a rotated bounding box regression model and a multi-task method for non-maximal suppression (NMS) between different classes. Experimental results on the public ship dataset HRSC2016 confirm that RR-CNN outperforms baselines by a large margin. |
关键词 | Rotated Region Convolutional Neural Network Ship Detection |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/14548 |
专题 | 空天信息研究中心 |
通讯作者 | Weng, Lubin |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Liu, Zikun,Hu, Jingao,Weng, Lubin,et al. Rotated Region Based CNN for Ship Detection[C],2017. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
rrcnn_final_v1.pdf(2750KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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