Rotated Region Based CNN for Ship Detection
文献类型：会议论文
| 作者 | Liu ZK(刘子坤)1,2 ; Hu JG(胡锦高)1,2; Weng LB(翁璐斌)1; Yang YP(杨一平)1; Weng LB(翁璐斌); Yang Yiping; Hu, Jingao; Liu, Zikun; Weng, Lubin
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| 出版日期 | 2017-10 |
| 会议日期 | 17-20 September 2017 |
| 会议地点 | China National Convention Center in Beijing, China |
| 关键词 | Rotated Region Convolutional Neural Network Ship Detection |
| 英文摘要 | 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/14548]  |
| 专题 | 自动化研究所_空天信息研究中心 |
| 通讯作者 | Weng LB(翁璐斌); Weng, Lubin |
| 作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Liu ZK,Hu JG,Weng LB,et al. Rotated Region Based CNN for Ship Detection[C]. 见:. China National Convention Center in Beijing, China. 17-20 September 2017. |
入库方式： OAI收割
来源：自动化研究所
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